行政院國家科學委員會專題研究計畫 成果報告
應用多目標規劃於台灣永續發展指標系統之研究
計畫類別： 個別型計畫 計畫編號： NSC93-2415-H-110-005- 執行期間： 93 年 08 月 01 日至 94 年 07 月 31 日 執行單位： 國立中山大學企業管理學系(所) 計畫主持人： 蔡憲唐 計畫參與人員： 童惠玲, 石孟勳 報告類型： 精簡報告 處理方式： 本計畫可公開查詢中 華 民 國 94 年 10 月 30 日
應用多目標規劃於台灣永續發展指標系統之研究
An Application of Multi-objective Programming to Indicator System of Sustainable Development in Taiwan
計畫編號：NSC 93-2415-H-110-005
執行期限：93 年 8 月 1 日~94 年 7 月 31 日 主持人：蔡憲唐 國立中山大學企業管理系
Abstract: Over the past few decades, the
striking levels of economic growth achieved have been accompanied by environmental degradation. Based on the characteristics of indicators on the commission on sustainable development of United Nations, this study constructs quantitative approaches to evaluate whether the historical data in Taiwan meets the relationship in DSR (Driving-force-State-Response) indicator framework of sustainable development or not. Moreover, this study also coordinates the sustainable development system to plan an optimal strategy path of sustainable development in Taiwan. The results are as expected. The multi-objective integrated model solved by goal programming for sustainable development is proposed to generate as a means for nation to understand what specific actions to take, in order to simulate the allocating results of national policy affecting the social, economic and environmental system and to measure progress towards sustainable development.
Keywords: sustainable development, DSR
framework, goal programming, green income, multi-objective integrated model
1. INTRODUCTION
The striking levels of economic growth achieved over the past few decades in Taiwan have been accompanied by environmental degradation, which exceeds the maximum loading of environmental self-purification and the carrying capacity of supporting ecosystems in this island. Efforts should obviously be made as early as possible to assist Taiwan to plan the strategies toward sustainable development. The classic definition of sustainable development, “meeting the needs of present
without compromising the ability of future generations to meet their needs”, was produced with the Brundtland report by the United Nation’s World Commission on Environment and Development entitled “Our Common Future” (WCED, 1987). This broad concept gained prominence at the 1992 United Nations Conference on Environment and Development in Rio De Janeiro, Brazil, now known as the Earth Summit (UN, 1992). The concept of sustainable development with its concern in the dynamic operation is for the ecosystem’s health, social justice, and ideals of responsibility to future generations. Such a broad conception is likely to give rise to various different interpretations, since people all have different goals and sensitivities. For example, Danaher (1998) states that the concept of sustainable development remains a multi-dimensional term and is increasingly becoming more important as a policy objective and as a policy tool. Krotscheck and Narodoslawsky (1998) illustrate that the social economic environmental research does not exclusively deal with ecological aspects of human activities, but includes social and economic factors on the same level. Spangenberg (2002) suggests that objectives of sustainable development defined for the economic, social, and environment dimension, but for sustainability characteristic they must be complemented by core institutional objectives. Giddings et al (2002) found that environment, society and economy are not unified entities, rather they are fractured and multi-layered and can be considered at different spatial levels. Caldwell (1984) and Moffatt (1994) have discussed problems of measuring and evaluating sustainability from the point views of ecology and environmental protection.
The Organization for Economic Cooperation and Development (OECD, 1991) has developed the application and research of the Pressure-State-Response (PSR) framework for environmental indicator and has also adapted the indicator strategies for sustainable development. The PSR framework indicates that the pressure from the human influences and activities, when combined with environmental conditions, causes environmental state change. As the environmental capacity changes, societal response policy tools include institution, regulations, financial measure or the change of management strategies. According to the Driving-Force-State-Response (DSR) characteristics of the indicators on the commission on Sustainable Development (CSD) of United Nations, the DSR indicator system is adapted to develop. The Driving-force-Pressure-State-Impact-Respon se (DPSIR) indicator system is based on the PSR framework. Such an empirical research of sustainable development is likely to give rise to various different approaches. For example, Walmsley (2002) applies the DPSIR indicator framework for developing indicator of sustainable development and for identifying key issues in catchment management in South Africa. Climate changes and air pollution with DPSER model (Yoon and Lee, 2003) are evaluated for sustainability of cities in Korea. Wei et al (2003) integrate the system of population, resource, environment and economy with multi-objective programming to measure sustainable development in Beijing. An operational approach has been given in Miranda (2001) to compare the relative sustainability and took the existing farming system as a case. A method is interrelated with the development of economy, environment and social quality in communities, which tracing stages of sustainable development of nations with integrated indicators (Zoeteman, 2001).
This research of DSR indicator framework is evaluated to make a significant contribution in enhancing the systematic test
among indicators and integrating the themes of sustainable development with multi-objective programming. Based on United Nations indicator framework of sustainable development, this initial research aims at building the indicator system of sustainable development in Taiwan, performing regression analysis for DSR system, finding key indicators of each individual dimension of sustainable development and developing the multi-objective integrated model for sustainable development. Based on the priority interchange of the objective function and the optimal balance of the system in seeking for the solution, the scenario analyses in each objective year setting (2006 and 2011) are developed to simulate the allocating results of national policy affecting the social, economic and environmental system. This paper explores that such a process generates as a means for nation to understand what specific actions to take in order to promote sustainable development.
2. VERIFICATION OF THE
RELATIONSHIP IN DSR SYSTEM FOR SUSTAINALBE
DEVELOPMENT
This paper is based on the DSR indicator framework on CSD of United Nations. Regarding Taiwan’s social-economic background and data acquirability, indicators are selected and modified to build the indicator system available for sustainable development in Taiwan (Figure 1). For testing the relationship in this DSR indicator system of sustainable development in Taiwan, nine regression equations are designed, where response variables are mostly the state indicators, and driving-force indicators or response indicators represent independent variables. Since the impact of each individual independent variable to response variable is different, the appropriate weight is therefore selected to sum up the variables represented for each dimension. Based on degree of freedom, independent variables
are used to sum up with the weight available with expert consulting in order to perform regression analysis. For the fit of regression equation, weight selection is based on the identification of regression test, that is, weight is objectively chosen with statistical test. Major weight is shown in Table 1. Data needed is mostly from the publication of National Statistics (1992-2001) in Taiwan. In data process, each regression equation is reformed to be the normalized translog equation. The findings illustrate that the regression fit in each equation is generally high and most regression parameters show significant. These results indicate that the historical data in Taiwan meets the relationship in DSR indicator framework of sustainable development. The detailed result of each regression equation is as following.
--- Figure 1 & Table 1 ---
2.1. Demographic dynamics and sustainability
W1= 0.000246 -1.7559 X11 + 1.5361Ｘ
12+ 1.5183 X13 (1)
The population density (W1) is
representative of the objective in this theme of demographic dynamics and sustainability. The decision variables are selected with the driving-force indicator of the population growth rate (X11), net migration rate (Ｘ12), and total fertility rate (Ｘ13), respectively. The findings of regression analysis indicate that the regression fit is very high (R2=0.9786) and the regression parameter is very significant. Meanwhile, the negative regression parameter of the population growth rate exhibits the fact that the population growth rate is smaller than the land area-increasing rate. The reason is that the rate of population growth at the end of year is relatively slower than of land area increasing (36,181.8718 km2 in 1997 and 36,188.0354 km2 in 1998 for one small island involved). The positive relationship of both net migration rate and total fertility rate indicates that the population density
increases as each indicator increases.
2.2. Protecting and promoting human health
W2= 0.000003 +0.8703 X21 (2) National health (W2) is assigned to agent response variable in the theme of protecting and promoting human health. This objective indicator is combined with four state indicators of saturation rate of tap water, life expectancy at birth, infant mortality rate, and maternal mortality rate. Independent variable is selected with the response indicator of total national health expenditure related to GDP (X21). The findings of regression analysis indicate that the regression fit is high (R2=0.7575) and the regression parameter is significant but less than 1. These results explore that total national health expenditure is inelastic, that is, national health only increases 0.87% as the share of total national health expenditure increases 1%.
2.3. Changing consumption patterns
W3= 2.77442E-11 -0.6387 X31 (3) The response variable is clean production (W3) combined with two state indicators of share of manufacturing value added in GDP and share of consumption of renewable energy resources. The resource quantity (X31) is independent variable, which is combined with two driving-force indicators of annual energy consumption and share of natural-resource intensive industries in manufacturing value-added. The findings of regression analysis indicate that the regression fit is very high (R2=0.9539) and the regression parameter is significant. The response of clean production to resource quantity is related negative.
2.4. Cooperation to accelerate sustainable development in countries and related domestic policies
W4=7.453088E-7+0.4975 X41 –0.5516Ｘ42 (4)
Green income (W4) is assigned to the agent response variable of this equation. Two independent variables are economic value-added (X41) and emissions of air pollution (X42). The former variable is the equal combination with GDP per capita, net investment share in GDP and sum of exports and imports as a percent of GDP; the latter variable is the equal combination with emissions of SO2 and emissions of NO2. The regression fit is very high (R2=0.9906) and each regression parameter is significant but elasticity is less than 1. These results indicate that green income increases about 0.5% as economic value-added increases 1%, while green income decreases about 0.55% as emissions of air pollution increase 1%.
2.5. The quality of freshwater resources
W5= -0.00609+0.3899 X51 –0.7419Ｘ52 (5) Biochemical oxygen demand in water bodies (BOD) (W5) is representative of quality of water resource. The independent variables are selected with two driving-force indicators of annual withdrawals of ground
and surface water (X51) and domestic
consumption of water per capita (X52). The findings of regression analysis indicate that the regression fit is high (R2=0.7896) and the relationship of annual withdrawals of ground and surface water and BOD is positive. BOD makes the self-purification capacity of water body down as annual withdrawals of ground and surface increases and increasing BOD shows the deterioration of water quality.
2.6. The usage of freshwater resources
W6= 0.00216+0.8829 X61 (6) Water consumption quantity (W6) is the agent response variable of this equation. This indicator is combined with two driving-force indicators of the above equation of water quality. The independent variable, water resource management (X61), is combined with wastewater treatment coverage (the combination of saturation rate of public sanitary sewer and pollution
control of business wastewater) and density of hydrological networks. The findings of regression analysis indicate that the regression fit is high (R2=0.7795) and regression parameter is significant. The positive relationship of the response for water consumption quantity to water resource management illustrates that the good mechanism for wastewater treatment can effectively promote the usage of water resource.
2.7. Protection of atmosphere in air pollution
W7= 0.0000013+0.9363 X71- 0.2378 X72 (7) Air pollution standard index (PSI) measured for the quality of air pollution (W7) is represented by the response variable of this equation. The larger PSI is, the more unhealthful the quality of air pollution is. The driving-force indicators of emissions of SO2 (X71) and emissions of NO2 (X72) are selected as independent variables. The findings of regression analysis indicate that the relation between emissions of nitrogen oxides and air quality is not significant, emissions of SO2 and deteriorative air quality are related positive, and regression parameter is close to 1. These results illustrate that deteriorative air quality follows the example of emissions of SO2 at each move and emissions of SO2 is the important one of main driving-forces of air pollution generated in empirical periods in Taiwan.
2.8. Protection of atmosphere for air pollution abatement
W8= -0.00000155+0.9106X81 (8) Pollution emissions (W8) is the agent response variable of this equation. This indicator is combined with two driving force
indicators of emissions of SO2 and
emissions of NO2. The decision variable is the response indicator of expenditure of air pollution abatement (X81). The findings of regression analysis indicate that the regression fit is high (R2=0.8292) and
regression parameter is significant. The positive relations between expenditure of air pollution abatement and pollution emissions has an impact on the effect which the increase degree of pollution emissions driven by macroeconomic activities in empirical periods is larger than the decrease degree of national expenditure of air pollution abatement. This impact leads to increased awareness that the national prevention policy against air pollution does not deserve to solve the problem of air pollution abatement in Taiwan.
2.9. Information for decision making
W9= 0.00000789+0.72799X91 (9) This theme indicates that better-informed citizens are more likely to be committed to the goal of sustainable development strategies, when the public can have access to the wide range of information. The objective variable of this regression equation is decision information (W9) combined mainly with two state indicators of main telephone lines for 100 inhabitants and access to information (the combination of home penetration rate of cable TV, number of newspaper per 100 households, and internet growth). The response indicator of programmes for national environmental
statistics (X91) is representative of
independent variable. This indicator signifies a country’s commitment to developing environmental statistics for use in national policy formulation and analysis. The number of annual official forms for reporting statistics of environment is transformed into the indicator of programmes for national environmental statistics. The regression result indicates the effect that programmes for national environmental statistics is not able to offer explanation for decision information.
3. TAIWAN’S COORDINATION OF SUSTAINABLE DEVELOPMENT SYSTEM
The methodology of this research is required to plan the optimal strategy path of
sustainable development in Taiwan. According to the characteristics of the system for sustainable development, the internal control variables are carefully selected at first, the regression function of each dimension is formulated, the equations of the intra-dimension are then combined, the integrated system of multiobjective programming for sustainable development is built and the scenario analyses with assigned priorities in various goal limits is taken. This system cannot only reflect the characteristics of each individual indicator in it, but also reflect the linkage among all indicators of intra-dimension. Under the monitoring of the framework of indicator system framework, the methods with synthesis and decomposition are used to coordinate the system for sustainable development.
According to the integrated system of multi-objective programming for sustainable development, the internal control variables include population growth rate, health expenditure related to GDP, GDP per capita, saturation rate of public sanitary sewer, pollution control of business wastewater, and expenditure of air pollution abatement. Meanwhile, population growth rate and health expenditure related to GDP are combined with the 4:6 weight to be health burden (X1) which is represented by key variable of social dimension; GDP per capita (X2) is representative of key variable of economic dimension; the saturation rate of public sanitary sewer and pollution control of business wastewater are combined with the weight of 6:4 to be wastewater treatment coverage (X3) which formulates key variable of water resource area in environmental dimension; expenditure of air pollution abatement (X4) is key variable of the other air area in environmental dimension. The priority parameter mainly contains structure of life expectancy at birth, allocation of green income per capita, improvement of water resource environment (BOD), and scatteration of ambient concentrations of pollutants. Based on the consequence of system analysis, these parameters are used to perform priority of each function in order
to obtain refined result.
The individual key variable in each social, economic, and environmental (including two areas of water resource and air) dimension describes the relationship of the two conditions and promoting each other between subsystems in a way of intra-dimension. These interactions among subsystems include connections among four dimensions. As linking indicator draws the outline of characteristics in itself, this system offers mathematical description of functions to the relations among decision variables. These relations are obtained by applying historical data to the statistical methods.
There are four objective variables (or response variables) setting in the coordination of sustainable development system － life expectancy at birth (Y1) in social dimension, green income per capita
(Y2) in economic dimension, and
biochemical oxygen demand (Y3) (BOD) as well as ambient concentrations of pollutants
(Y4) in environmental dimension. The
decision variables of each individual dimension contain as following: social dimension (the objective function of social health) – health burden (the combination of population growth rate and health expenditure related to GDP), GDP per capita, and expenditure of air pollution abatement; economic dimension (the objective function of green production)－GDP per capita, and expenditure of air pollution abatement; environmental dimension (the objective function of water quality)－GDP per capital and wastewater treatment coverage (the combination of saturation rate of public sanitary sewer and pollution control of business wastewater); environmental dimension (the objective function of air quality)－GDP per capita and expenditure of air pollution abatement. These decision variables affect the objective functions and then are converted into the corresponding flexible goals. The interactive system of intra-dimension is formulated.
For establishing the constraint equation to develop the multi-objective model of integrating the sustainable development system in Taiwan, the regression analysis of intra-dimensional indicator system of sustainable development is necessary to be taken at first (Table 2). The findings are shown as the following results: the fit of regression equation mostly shows significant; the direction of parameter sign is required to meet the economic logic. The elasticity of health burden in the equation of life expectancy at birth is -0.74551. This result shows the negative relationship between health burden and life expectancy at birth and also indicates the situation that the heavier health burden is, the lower life expectancy at birth is. The elastic parameter of GDP per capita and green income per capita close to 1 (0.99861) reflects the relationship that the latter indicator follows the example of the former indicator at each move. Each indicator of environmental dimension (such as BOD and ambient concentrations of pollutants) and is negatively related to GDP per capita. This relationship indicates that environmental quality in Taiwan has significantly improved by stricter environmental measure (such as expenditure of air pollution abatement and standard index of water quality) in these years and it appears the decoupling between economic growth and environmental pollution. Based on the process of the regression equation brought into the model of goal programming, the scenario analyses of this research is required to take with the priority arrangement of each individual dimension in order to obtain the sensitivity and robust of planning decision variable with objective change. There are three scenario analyses － GWAS, WSGA, and SAWG, respectively. What is called GWAS is referred to first consideration that green income per capita (G) in economic dimension is improved by government, second consideration of improving water quality (W) and air quality (A) in environmental dimension after satisfying the first consideration of green production
function, and last consideration of satisfying the demand of social health (S) in social dimension. The rest may be deduced by analogy.
--- Table 2
--- The data in empirical analysis of the past 14 years from 1989 to 2002 is in accordance with relating statistic data announced by National Statistics in Taiwan. The software of SAS/LP is applied to solve the problem of the multi-objective model. To minimize the deviation variable of the multi-objective model is stated as follows: Min Z =
{
λ1(n2),λ2(p3),λ3(p4),λ4(n1)}
s.t. －0.74551x1＋0.52152x2＋0.47892x3＋n1－ 1 p =Y1 0.99861x2＋0.01382x4＋n2－p2=Y2 －1.72037x2＋0.85525x3＋n3－p3=Y3 －0.87432x2－0.20837x4＋n4－p4=Y4 0 = × k k p n 0 , k ≥ k p n (k= 1, …, 4),where constraints are represented by four goals of the dimension:
Y1：life expectancy at birth of social health model in social dimension;
Y2：green income per capita of green production model in economic dimension;
Y3：BOD of water resource quality model in environmental dimension;
Y4：ambient concentrations of pollutants of air pollution quality model in environmental dimension.
The agent decision variable in four dimensions is as following:
X1：health burden of social health model in social dimension;
X2：GDP per capita of green production model in economic dimension;
X3：wastewater treatment coverage of water resource quality model in environmental dimension;
X4：air pollution expenditure per capita of air pollution quality model in environmental dimension;
nk： the underachievement of goal k, called negative deviation variable;
pk：the overachievement of goal k, called positive deviation variable;
0 =
× k
k p
n : the one and only negative
or positive deviational variable existing in any constraint, not both positive and negative deviational variable existing at the same time;
k
λ (n)：the priority factor assigned to the negative deviational variables;
k
λ (p)：the priority factor assigned to the positive deviational variables.
By applying the above models, the optimal goal limits designed for 2006 and 2011 are selected in accordance with the relating goals of national medium-term programs and long-term programs. On the other hand, each initial value of decision variables is based on the formal value in 2002. Each individual solution in different state of the solving process is obtained in accordance with priority interchange on each situation. Future population growth rate in scenarios analysis is assumed to be three considerations – low, medium, and high level, respectively. The simulated result of the multi-objective programming driven by the system is summarized as Table 3 and Table 4 and is stated as follows:
--- Table 3 & Table 4 --- 3.1. Scenario A－GWAS )} ( ), ( ), ( ), ( { 1 n2 2 p3 3 p4 4 n1 Z Min = λ λ λ λ (10)
The model in this scenario analysis is as the above equation and the result of seeking for solution of multi-objective programming is stated follow.
population growth rate is going to be held increasing and health expenditure is going to be kept growing. In addition, GDP per capita planned with highest priority of the formal green production function grows more than three times to reach NT$1,589,779, while population growth rate is low (0.49%) and total national health expenditure related to GDP is large (7.46%) as Table 4 shown. The requirement of environmental quality is also adapted to take more active measures such as number control led of business wastewater pollution needed to lower extensively on the one hand (from 12,689 business units down to 4,131 business units) and saturation rate of public sanitary sewer also needed to raise extensively on the other hand (from 10.10% up to 18.59%); under the same assumption as the above case in 2006, total national health expenditure related to GDP in 2011 is required to reach 9.51% as well as GDP per capita is also required to reach NT$ 2,715,946 which is about six times of the amount in 2002, while the population growth rate in 2011 is planned to be 0.34% at low level. In the same way, the environmental quality is required to go on taking active measures.
Each goal achieved in this scenario analysis is shown as Tables 3. Life expectancy at birth in social dimension maintains the same optimal value of goal in each objective year -- age of 78 in 2006 and age of 79 in 2011. These results in social dimension are led to the lowest priority objective function of social health constrained. In the highest priority of the formal green production function, green income per capita in 2006 reaches NT$1, 461, 753 to a high degree, and this income level in 2011 appears beyond the imaginary amount of NT$ 2,530,540.
The goal of environmental dimension is highly related with BOD and with GDP per capita, respectively. Under the highest priority objective function of green production, both goals of water resource quality in 2006 and in 2011 appear super clean almost without pollution (BOD is
0.048 mg/l in 2011). In a series of influence, air quality is also shown the same appearance with low pollution.
As a whole, green income per capita is the major planned goal with the highest priority in this scenario analysis. Hence, the high level of green income per capita in economic dimension is obtained; the relationship of chain reaction and feedback among the objective functions has made a greater influence in the goals of environmental dimension. For life expectancy at birth in social dimension is the planned goal with the lowest priority, the sensitivity is low.
3.2. Scenario B－WSGA
MinZ =
{
λ1(p3),λ2(n1),λ3(n2),λ4(p4)}
(11) The model in this scenario analysis is as the above equation and the result of seeking for solution in the system is stated as follows.In social dimension, total national health expenditure related to GDP raises up to 6.99% in 2006 an to 8.26% in 2011, respectively, while the planned value of the population growth rate in the objective year is controlled at 0.54% in 2006 and at 0.42% in 2011. Meanwhile, GDP per capita in economic dimension in 2006 evaluated by the initial value increases 29%; GDP per capita in 2011 evaluated in the same way increases about 36%. Saturation rate of public sanitary sewer in water resource quality area of environmental dimension reaches the range of 27.4% in 2006, and 75.6% in 2011, respectively, while the improvement degree of wastewater treatment coverage is cut down to 12.87% in 2006, and 17.74% in 2011, respectively. Expenditure of air pollution abatement per capita in the other air quality area of environmental dimension is NT$ 271 in 2006 and appears the raising value near two times in 2011.
Even though water resource quality is the major planned goal with the highest
priority in this scenario analysis, BOD improved is only planned as 1.8159 mg/l in 2006 and 1.3835 mg/l in 2011 by the impact of the third high priority of green production function. GDP per capita reaches NT$ 507,038 in 2006, and NT$ 540,007 in 2011 to a small extent.
Life expectancy at birth in social dimension has little impact as the same goal (age of 78 in 2006, and age of 2011, respectively). As a whole, each of four objective functions is individually satisfied within its formal feasible area of goal limits. Both life expectancy at birth and ambient concentrations of pollutants reach their goals. BOD with the highest priority of the formal water resource quality function reflects the best environmental situation of water quality. The improvement range is raised up to 54.6% in 2006, and 60.47% in 2011. Given the objective value of both environmental and social dimensions, GDP per capita in the subsystem of green production is raised to a small extent.
3.3. Scenario C－SAWG
MinZ =
{
λ1(n1),λ2(p4),λ3(p3),λ4(n2)}
(12) The model in this scenario analysis is as the above equation, and the result of seeking for solution in the system is stated as follows.In the social dimension, total national health expenditure related to GDP raises up to 6.51% in 2006 and to 7.08% in 2011 to a low degree, while the population growth rate is controlled at the higher level of 0.6% in 2006, and of 0.53% in 2011, respectively. Meanwhile, GDP per capita in economic dimension in 2006 evaluated by the initial value increases 7.14%, and GDP per capita in 2011 evaluated in the same way increases about 19.94%. In the context of wastewater treatment coverage in the environmental dimension, number of business listed for pollution control of wastewater is planned less than 1% (0.08%) of the curtailed content, while saturation rate of public sanitary sewer shrinks under 5% in both two
objective years. Expenditure of air pollution abatement per capita with the next highest priority of the formal function increases to a high degree in order to improve the air quality.
While social health in social dimension is the objective function with the highest priority in this scenario analysis, the goal of life expectancy at birth increases age of one in each objective year. In the context of green production function with the lowest priority to create impact on the growth extent of GDP per capita, the curtailment of GDP per capita is raised up. This curtailing extent in economic dimension only influences the improvement degree of water resource quality (the curtailing degree of BOD is 40.93% in 2006 and 52.62% in 2011). Although expenditure of air pollution abatement increases to a large extent, the improvement of air quality is finally raised up in 2011 under the condition of the growth restraint of green income per capita. As a whole, green income per capita is raised up to decline. Hence, all of the goals in this scenario analysis are not the optimal planned solution.
To summarize the above results of three scenarios analysis, when green income per capita based on the different rate of population growth is planned with the highest priority green production function, the planned value at high level of green income per capita and environmental quality in Scenario A (GWAS) cannot be reached in the current economy. The growth of green income per capita is declined in Scenario C (SAWG). This negative growth of green income per capita is against the rule of sustainable development. Regarding Scenario B (WSGA), the growth of green income per capita is raised to optimal planned extent and water resource quality is improved to a stable degree. Two above situations in this scenario are in the feasible economic area. These activities of national health, green production, and ecosystem seek to take advantage of important supporting resource in the government’s
decision-making and for the sake of our nation’s better tomorrows.
4. CONCLUSION AND SUGGESTION
This paper explores the building of framework on DSR indicator system of sustainable development in Taiwan. It is based on the Commission on Sustainable Development (CSD) Working List of Indicators of the United Nations in 1996. This is the study including quantitative approaches to evaluate the DSR indicator system. In the context of the results in this research, the historical data in Taiwan meets the relationship in DSR indicator framework of sustainable development. These results also indicate that the complex system in this research can be used to develop the integrated framework of system with cause-effect logic and feedback for making the program of sustainable development at national level in Taiwan. The characteristics of this paper can be attributed to three parts. First, the future development state of each individual subsystem can be planned in advance at the given goals and the intra-dimensional constraints in order to offer decision maker scientific decision basis. Secondly, the whole integrated model that reflects the linkage mechanism among key variables of each individual subsystem is built by using the approach of goal programming and is made in all directions. Finally, for an empirical study, this paper is not only required to evaluate the models in theory, but also ascertain the feasibility and affectivity of the goals by application.
In the view of the above, for this sustainable development system, a multi-objective integrated model is formulated and solved by goal programming technique. The test of regression fit between decision variables and objectives is taken at first. The response variables in four dimensions of sustainable development are carefully selected to be life expectancy at birth, green income per capita, water resource quality (BOD), and air quality (PSI). Each of the above variables is
required to set goal limits based on the objective years in 2006 and in 2011. The scenario analysis is realized with assigned priority change of objective function. The findings illustrate that the scenario for green income (Scenario B) accompanied by the highest priority of environmental quality is optimal in the condition of moderate-driven rate of population growth, if green income per capita is considered to be the core set. According to the given level of environmental quality, the planning value of green income per capita is NT$ 507,038 in 2006 and NT$ 540,007 in 2011, respectively. The growth rate of this indicator calculated at the initial value in 2002 also increases 6.6% in 2006 and 13.6% in 2011, respectively. Based on the continuity of policy implementation and the limit for major observed variables to a small degree in a short time, each variable in scenario B is found to meet the reasonable growth extent. For the multiobjective planning model in this research, the results of scenario for green income are satisfied to be the optimal plan. These findings will be made available to country to assist it in its efforts to measure progress toward sustainable development.
References
1. Caldwell, L.K. (1984) ‘Political aspects of ecologically sustainable development’, Environment Conservation, Vol. 11, No. 4, pp. 299-308.
2. Danaher, M. (1998) ‘Towards sustainable development in Japanese
environmental policy-making’, Sustainable Development, Vol. 6, pp.
101-110.
3. Giddings, B., Hopwood, B. and O’Brien, G. (2002) ‘Environment, economy and Society: Fitting them together into sustainable development’, Sustainable Development, Vol. 10, pp. 187-196. 4. Krotscheck, C. and Narodoslawsky, M.
(1998) ‘Social economic environmental research in Austria’, Sustainable Development, Vol. 6, pp. 31-39.
5. Miranda, J.I. (2001) ‘Multicriteria analysis applied to the sustainable
agriculture problem’, The International Journal of Sustainable Development and World Ecology, Vol. 8, No. 1, pp. 67-77. 6. Moffatt, I. (1994) ‘On measuring
sustainable development indicators’, The International Journal of Sustainable Development and World Ecology, Vol. 1, No. 2, pp. 102-112.
7. Spangenberg, J. H. (2002) ‘Institutional sustainability indicators: An analysis of the institutions in Agenda 21 and a draft set of indicators for monitoring their effectivity’, Sustainable Development, Vol. 10, pp. 103-115.
8. UNCSD (UN Commission on Sustainable Development) (1996) Indicators of Sustainable Development: Framework and Methodologies, New York.
9. UNCED (1992) Agenda 21, Earth Summit, Rio de Janeiro.
10. Walmsley, J.J. (2002) ‘Framework for measuring sustainable development in catchment systems’, Environmental
Management, Vol. 29, No. 2, pp. 195-206.
11. WCED (World Commission on Environment and Development) (1987) Our Common Future, Oxford University Press: Oxford.
12. Wei, Y. M., Tsai, H. T., Fan, Y. and Zeng, R. (2003) ‘Beijing’s co-ordination development of population, resources, environment, and economy’, The International Journal of Sustainable Development and World Ecology, accepted.
13. Yoon, S.W. and Lee, D.K. (2003) ‘The development of the evaluation model of climate changes and air pollution for sustainability of cities in Korea’, Landscape and Urban Planning, Vol. 63, pp. 145-160.
14. Zoeteman, K. (2001) ‘Sustainability of nations’, The International Journal of Sustainable Development and World Ecology, Vol. 8, No. 2, pp. 93-109.
--Main telephone lines per 100 inhabitants (S) -- Access to information (S)
-- Programmes for national environmental statistics (R)
Indicators for sustainable development in
T aiwan Society Economy Environment Institution
Demographic dynamics and sustainability
Protecting and promoting human health
Changing consumption patterns
Cooperation to accelerate sustainable development in countries and related domestic policies
The quality of freshwater resources
Protection of atmosphere in air pollution
Information for decision-making
--Population growth rate (D) --Net migration rate (D) --Total fertility tare (D) --Population density (S)
--- Access to safe drinking water (S) -- Life expectancy at birth (S) -- Infant mortality rate (S) --Maternal mortality rate (S)
--Total national health expenditure related to GNP (R)
--Annual energy consumption (D) --Share of natural resource intensive in
manufacturing value-added (D) --Share of consumption value-added in
GDP (D)
--Share of consumption of renewable energy resource (S)
--GDP per capita (D)
--Net investment share in GDP (D) --Sum of exports and imports as a percent
of (D)
-- Emissions of sulphur oxides (D) -- Emissions of nitrogen oxides (D) --Environmentally adjusted Net Domestic
Product (S)
-- Annual withdrawals of ground and surface water (D)
-- Domestic consumption of water per capita surface water (D)
--Biochemical oxygen demand in water bodies (S)
-- Ambient concentrations of pollutants in urban areas (S)
-- Expenditure on air pollution (R) The usage of freshwater
resources
-- Annual withdrawals of ground and surface water (D)
-- Domestic consumption of water per capita surface water (D)
-- Wastewater treatment coverage (R) -- Density of hydrological networks (R) -- Emissions of sulphur oxides (D) -- Emissions of nitrogen oxides (D) --Ambient concentrations of pollutants (S)
Protection of atmosphere for air pollution abatement
Table 1 The weight selection of objective and decision variable for nine regression equation
Category Regression
equation Objective Decision variable
Demographic dynamics and sustainability
W1：Population density X11：Population growth rate
X12：Net migration rate
X13：Total fertility rate
SOCIAL
Protecting and promoting human
health
W2： National health
-saturation rate of tap water (25%) -life expectancy of birth (25%)
-infant mortality rate (25%) -maternal mortality rate (25%)
X21：Total national health expenditure related to GDP.
Changing consumption patterns
W３：Clean Production
-share of manufacturing value-added in GDP (80%) -share of consumption of renewable energy resources (20%)
X３1：Resource quantity
-annual energy consumption (50%)
-share of natural-resource intensive industries in manufacturing value-added (50%)
ECONOMIC Cooperation to
accelerate sustainable development in countries and related
domestic policies
W4：Environmentally adjusted net domestic product X41：Economic value-added
-GDP per capita (1/3)
-net investment share in GDP (1/3)
-sum of exports and imports as a percent of GDP (1/3) X42：Environmental value-added
-emissions of SO2 (50%)
-emissions of NO2 (50%)
The quality of freshwater resources
W5：Biochemical oxygen demand in water bodies (BOD) X51：Annual withdrawals of ground and surface water.
X52：:Domestic consumption of water per capita.
ENVIRONMENTAL
(WATER) The usage of
freshwater resources
W6：Water consumption quantity
-annual withdrawals of ground and surface water (50%) -domestic consumption of water per capita (50%)
X61：Water resource management
-density of hydrological networks (50%) -waste-water treatment (50%)
‧saturation rate of public sanitary sewer (2/3) ‧pollution control of business waste-water (1/3) Protection of
atmosphere in air pollution
W7：Ambient concentrations of pollutants (PSI > 100) X71：Emission of SO2
X72：Emission of NO2
ENVIRONMENTAL
(AIR) Protection of
atmosphere for air pollution abatement
W8：Pollution emissions
- emissions of SO2 (80%)
- emissions of NO2 (20%)
X81：Expenditure on air pollution abatement
INSTITUTIONAL Information for
decision-making
W9：decision information
-main telephone lines per 100 inhabitants (30%) -access to information (70%)
‧home penetration rate of cable TV (15%) ‧number of newspaper per 100 households (25%) ‧internet growth (60%)
Table 2 The models in the intra-dimension of sustainable development Model X1 X2 X3 X4 R2 p-value Social health Y1 -0.7455* 0.52152* 0.47892 0.8058 0.0007 Green production Y2 0.99861*** 0.01382** 0.9998 <0.0001 Water resource quality Y3 -1.72037 *** 0.85525* 0.8920 <0.0001 Air quality Y4 -0.87432*** -0.20837 0.8394 <0.0001 ***P<0.001 **P<0.01 *P<0.05
Table 3 Comparisons between ideal goal limits and computational results of three scenarios
2006 2011 Goal Objective
value GWAS WSGA SAWG
Objective
value GWAS WSGA SAWG
Y1：Life expectancy at birth 78 78 78 79 79 79 79 80
Y2：Green income per capita 475,566 1,461,753 507,038 424,401 535,957 2,530,540 540,007 478,295
Y3：BOD(mg/L) 4 0.0484 1.8159 2.3629 3.5 0.0089 1.3835 1.6558
Y4：Quality of air pollution (PSI>100)
(%) 2 0.5744 2 2 1.5 0.2431 1.5 1.5
Table 4 Solutions of decision variables for three scenarios
2006 2011 Decision variable
Initial value
(2002) GWAS WSGA SAWG GWAS WSGA SAWG
X1：Health burden
---Population growth rate (40%) ---Total national health expenditure
related to GDP (60%) 0.53% 5.89% 0.49% 7.46% 0.54% 6.99% 0.6% 6.51% 0.34% 9.51% 0.42% 8.26% 0.53% 7.08% X2：GDP per capita (NT$) 432,239 1,589,779 556,852 463,120 2,715,946 587,880 518,426
X3：Wastewater treatment coverage
--- Pollution control of business wastewater (40%)
--- Saturation rate of public sanitary sewer (60%) 12,689 10.10% 4,131 18.59% 5,576 12.87% 12,585 4.75% 2,810 29.82% 4,292 17.74% 12,585 4.75%
X4：Expenditure of air pollution abatement
per capita (NT$) 28 63 271 727 63 529 1,037
Decision variable Goal
