An integrated sustainability decision-support framework Part I: Problem structuring

One of the main goals in decision-making for sustainable development is to identify and choose the most sustainable option from among different alternatives. This process usually involves a large number of stakeholders with multiple, often conflicting, objectives. Facilitating and resolving such difficult decision situations can be complex, so that a more formal and systematic approach to decision-making may be necessary. This two-part paper proposes an integrated multiple criteria decision-support framework specifically developed to provide systematic, step-by-step guidance to decision-makers. The framework, which is suitable for both corporate and public policy-making in the context of sustainable development, comprises three steps: problem structuring, problem analysis and problem resolution. In this paper, the focus is on problem structuring while Part II concentrates on problem analysis and resolution. Problem structuring includes identification of stakeholders, sustainability issues and indicators relevant for a particular decision problem. Sustainability indicators are used as decision criteria for identifying and choosing the most sustainable option. In the problem analysis step, decision makers articulate their preferences for different decision criteria. A suitable Multiple Criteria Decision Analysis (MCDA) technique, such as multi-objective optimisation, goal programming, value-based and outranking approaches, is then used to model the preferences. These techniques are discussed in Part II, which also gives guidance on the choice of the most appropriate MCDA method. Based on the outcome of preference modelling, which estimates the overall 'value' of each alternative being considered, decision-makers can then choose the 'best' or most sustainable option. Such an integrated decision-support framework is useful for providing structure to the debate, ensuring dialogue among decision-makers and showing trade-offs between conflicting objectives. In this way, it may be possible to create shared understanding about the issues, generate a sense of common purpose, and often, resolve 'difficult' decision problems.     

An integrated sustainability decision-support framework Part II: Problem analysis

One of the main goals in decision-making for sustainable development is to identify and choose the most sustainable option among different alternatives. This process usually involves a large number of stakeholders with multiple, often conflicting objectives. Facilitating and resolving such difficult decision situations can be complex, so that a more formal and systematic approach to decision-making may be necessary. This paper proposes an integrated multiple criteria decision-support framework specifically developed to provide a systematic, step-by-step guidance to decision-makers. The framework, which is suitable for both corporate and public policy-making in the context of sustainable development, comprises three steps: problem structuring, problem analysis and problem resolution. This paper concentrates on problem analysis and resolution, where decision-makers articulate their preferences for different decision criteria. A suitable Multiple Criteria Decision Analysis (MCDA) technique, such as multi-objective optimisation, goal programming, value-based and outranking approaches, is then used to model the preferences. These techniques are discussed here in some detail, to provide guidance on the choice of the most appropriate MCDA method. Based on the outcome of preference modelling, which estimates the overall 'value' of each alternative being considered, decision-makers can then choose the 'best' or most sustainable option. Such an integrated decision-support framework is useful for providing structure to the debate, ensuring dialogue among decision-makers and showing trade-offs between conflicting objectives. In this way, it may be possible to create shared understanding about the issues, generate a sense of common purpose and, often, resolve 'difficult' decision problems.     

A GIS-based moving window analysis of landscape pattern in the Beijing metropolitan area,China

Quantifying landscape pattern and its change is essential for monitoring and assessment of ecological consequences of urbanization. Using the GIS-based land-use data for 2002, we combined moving window analysis along an urban-rural gradient and around the urban centre with landscape metrics to quantify the spatial pattern of urbanization in Beijing. The results of moving window analysis along the urban-rural gradient indicated that, for class-level metrics, the spatial pattern of urbanization could be quantified using landscape metrics, different land-use types exhibited distinctive spatial signatures, and, for landscape-level metrics, the increase in urbanization in the metropolitan Beijing region has resulted in dramatic increases in patch density (PD), edge density (ED), and patch and landscape shape complexity, and sharp decreases in the largest and mean patch size (MPS), agriculture land-use type, and landscape connectivity. The results of moving window analysis around the urban centre showed that the direction of urbanization could be quantified using the class-level metrics, and landscape-level metrics indicated similar results to gradient analysis. In general, moving window analysis showed that the increasingly urbanized landscape became compositionally more diverse, geometrically more complex, and ecologically more fragmented.     

Contrasting visions of food and farming sustainability: NGOs versus the UK Labour government

In a previous issue of the International Journal of Sustainable Development and World Ecology I described how competing visions of sustainable agriculture are currently battling for hegemonic status (Johnson 2006). One vision seeks to break the hegemony of productionism and the other seeks to continue down its path with a greater emphasis on biotechnology. In this paper, I wish to explore these visions further as they apply to actual existing strategies for sustainable farming and food in the UK. This paper uses discourse analysis to compare the UK government's vision for sustainable farming and food sectors with that of two NGOs: Friends of the Earth and Corporate Watch. The language and discourse in the government's and NGOs' strategy documents is examined for evidence indicating their ideological conception of agricultural sustainability. The paper concludes that, whilst the NGOs are pursuing a strong vision of sustainability, seeking to break productionism's hegemony, the UK government is pursuing an ideologically weak-tomoderate conceptualisation of sustainability, pursuing the high-technology modified version of agricultural productionism.     

Multi-scale sustainability evaluation of natural resource management systems: Quantifying indicators for different scales of analysis and their trade-offs using linear programming

The purposes of this paper are: (a) to describe a framework designed for multi-scale sustainability evaluation of Natural Resource Management Systems (NRMS), and (b) to illustrate its application for quantitative analysis using linear programming. The framework described here is intended to contribute to the operationalisation of the concept of sustainability by supporting the processes of design, evaluation and implementation of alternative NRMS at different scales. In this paper, Linear Programming is used for the quantitative analysis of indicators and their trade-offs; using a schematised example, the basic characteristics of the Multi-scale Multiple Goal Linear Programming (M-MGLP) method are described. In M-MGLP, indicators pertaining to different scales of analysis can be set as objectives or constraints for the optimisation. In this way, stakeholders interacting in a specific region can be made aware of the consequences of alternative NRMS in terms of the different indicators at the same scale and/or for indicators at other scales of analysis. The paper ends with a discussion of the main strengths and limitations of the framework and, specifically, of linear programming.     

Under the shadow of Asian Brown Clouds: Unbalanced regional productivities in China and environmental concerns

China has seen rapid economic growth over the past two decades, but severe environmental problems have accompanied this, such as the looming danger of Asian Brown Clouds. This paper analyzes the regional development of China by examining economic performance and environmental factors. Technical efficiency and productivity changes in 31 regions of China are computed for the period 1997–2001. In the case of regional GDP, the fast-developing eastern (coastal) regions experience higher technical efficiency and productivity growth than the inland central and western regions. When environmental factors are incorporated, the eastern regions still perform better than inland regions, both from static and dynamic analysis. This phenomenon is termed the 'double deterioration' of the inland areas in China. Double deterioration is attributed to the lack of economic resources to replace highly-polluting production equipment and technology in those less developed regions.     

Socio-environmental co-evolution: some ideas for an analytical approach

A number of scholars, mainly in ecological economics, have been attracted to the concept of co-evolution for the analysis of socio-environmental change. Yet none has adopted and developed an applied analytical approach using an explicitly evolutionary framework. This paper discusses how other scholars in economics, technological studies, organization and political science have been using evolutionary explanations and draws some lessons for ecological economics. Evolutionary is a mode of explanation based on the selective retention of renewable variation. It accounts for phenomena of structural fit and change in a variety of domains. A co-evolutionary explanation, by extension, entails two or more evolving systems whose interaction affects their evolution. Socio-environmental co-evolution involves human systems (material practices and non-material ideas and values) and non-human systems (living and physical). The challenge then is how to develop case-specific, empirical applications that define and elaborate the variants that co-evolve and specify the processes of mutual selection. Applications could benefit from existing classifications and causal propositions in the natural and social sciences. Co-evolution is part of a larger analytical toolkit for looking at complex socio-environmental problems. Although distinct, there are strong synergies, complementarities and potential for combined uses between co-evolutionary, co-dynamic and complexity-based explanations.     

Some approaches to the construction of a sustainable development index for the Philippines

Principal Component Analysis (PCA) is a convenient tool used in aggregating the indicators of sustainable development and providing indices where different weights are assigned to the various indicators. There are, however, problems in interpreting of indices, especially if time series data are used. This study explores the feasibility of applying recent developments in PCA of time series using Philippine data. We present the comparative advantages of SPCA (Sparse Principal Component Analysis) relative to averaging of an adequacy/inadequacy index and PCA in index construction from various indicators of sustainable development in the Philippines in terms of usefulness and validity of indices being developed. SPCA can attain sparse and non-overlapping loadings without losing a large amount of explained variance compared to PCA. Because of the non-overlapping contribution of variables in SPCA components, indices can have clear and mutually exclusive meanings, facilitating interpretation. Even with a more complicated algorithm, reduced dimensions and simpler interpretation of indices justify the advantages of SPCA over PCA in index construction. The indices are interpreted in terms of the milestone of sustainability in the Philippines. The resulting indices provide an adequate summary of the sustainable indicators and evidence of the importance of leadership and political will in sustainable development.     

Efficient air pollution abatement for regions in China

This paper computes the efficient air pollution abatement ratios of 30 regions in China during the period 1996–2002. Three air emissions (SO₂, soot and dust) are considered. Data envelopment analysis (DEA) with a single output (real GDP) and five inputs (labour, real capital stock, SO₂, dust and soot emissions) is used to compute the target emissions of each region for each year. The efficient abatement ratios of each region in each year are then obtained by dividing the target emission by the actual emission of an air pollutant. Our major findings are: 1. The eastern area is the most efficient region with respect to SO₂, soot and dust emissions in every year during the research period. 2. The eastern, central and western areas have the lowest, medium and highest 1996–2002 average target abatement ratios of SO₂ (22.09%, 42.23% and 57.58%), soot (26.19%, 56.34% and 66.37%) and dust (15.20%, 29.09% and 40.59%), respectively. 3. These results are consistent with the Environmental Kuznets Curve (EKC) theory, whereby a more developed area will use environmental goods more efficiently than a less developed area. 4. Compared to dust emission, the average target abatement ratios for SO₂ and soot emissions (as direct outcomes of burning coal) are relatively much higher for all three areas.     

Monitoring sustainable agriculture in Southeast Asia

Monitoring agricultural sustainability requires careful summarising of indicators collected over time into indices representing facets of sustainability. When the number of variables exceeds the number of observations, interpretation of components from ordinary principal component analysis is usually difficult because of minimal or absence of sparsity among the loadings. This is also true for time series indicators that exhibit non-stationarity. A framework for the assessment of agricultural sustainability in a regional context is proposed. Sparse principal component analysis is used in constructing indices of agricultural sustainability that are then used to characterise the state of agricultural development and the dynamics of agricultural growth in Southeast Asia.