Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont

Multicriteria decision analysis (MCDA) provides a well-established family of decision tools to aid stakeholder groups in arriving at collective decisions. MCDA can also function as a framework for the social learning process, serving as an educational aid in decision problems characterized by a high level of public participation. In this paper, the framework and results of a structured decision process using the outranking MCDA methodology preference ranking organization method of enrichment evaluation (PROMETHEE) are presented. PROMETHEE is used to frame multi-stakeholder discussions of river management alternatives for the Upper White River of Central Vermont, in the northeastern United States. Stakeholders met over 10 months to create a shared vision of an ideal river and its services to communities, develop a list of criteria by which to evaluate river management alternatives, and elicit preferences to rank and compare individual and group preferences. The MCDA procedure helped to frame a group process that made stakeholder preferences explicit and substantive discussions about long-term river management possible.     

Building Consensus in Environmental Impact Assessment Through Multicriteria Modeling and Sensitivity Analysis

Multicriteria decision analysis (MCDA) increasingly is being applied in environmental impact assessment (EIA). In this article, two MCDA techniques, stochastic analytic hierarchy process and compromise programming, are combined to ascertain the environmental impacts of and to rank two alternative sites for Mexico City’s new airport. Extensive sensitivity analyses were performed to determine the probability of changes in rank ordering given uncertainty in the hierarchy structure, decision criteria weights, and decision criteria performances. Results demonstrate that sensitivity analysis is fundamental for attaining consensus among members of interdisciplinary teams and for settling debates in controversial projects. It was concluded that sensitivity analysis is critical for achieving a transparent and technically defensible MCDA implementation in controversial EIA.     

Trends and applications of multi-criteria decision analysis in environmental sciences: literature review

Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature.     

A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making

The choice among alternative water supply sources is generally based on the fundamental objective of maximising the ratio of benefits to costs. There is, however, a need to consider sustainability, the environment and social implications in regional water resources planning, in addition to economics. In order to achieve this, multi-criteria decision analysis (MCDA) techniques can be used. Various sources of uncertainty exist in the application of MCDA methods, including the selection of the MCDA method, elicitation of criteria weights and assignment of criteria performance values. The focus of this paper is on the uncertainty in the criteria weights. Sensitivity analysis can be used to analyse the effects of uncertainties associated with the criteria weights. Two existing sensitivity methods are described in this paper and a new distance-based approach is proposed which overcomes limitations of these methods. The benefits of the proposed approach are the concurrent alteration of the criteria weights, the applicability of the method to a range of MCDA techniques and the identification of the most critical criteria weights. The existing and proposed methods are applied to three case studies and the results indicate that simultaneous consideration of the uncertainty in the criteria weights should be an integral part of the decision making process.     

Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods

The main objective of this research is to model the uncertainty associated with GIS-based multi-criteria decision analysis (MCDA) for crop suitability assessment. To achieve this goal, an integrated approach using GIS-MCDA in association with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) were applied for Saffron suitability mapping in East-Azerbaijan Province in Iran. The results of this study indicated that integration of MCDA with MCS and GSA could improve modeling precision by reducing data variance. Results indicated that applying the MCS method using the local training data leads to computing the spatial correlation between criteria weights and characteristics of the study area. Results of the GSA method also allow us to obtain the priority of criteria and identify the most important criteria and the variability of outputs under uncertainty conditions for model inputs. The findings showed that, commonly used primary zoning methods, without considering the interaction effects of variables, had significant errors and uncertainty in the output of MCDA-based suitability models, which should be minimized by the advanced complementarity of sensitivity and uncertainty analysis.     

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.     

Estimating monthly total nitrogen concentration in streams by using artificial neural network

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.     

Multicriteria Decision Aid in Supporting Decisions Related to Groundwater Protection

In the recent decades, there have been frequent conflicts between groundwater resources and environmentally hazardous activities. An example is the construction of the new highway through the important glaciofluvial esker aquifer Nybroåsen, which passes the protection zone of the water supply for the Kalmar municipality, in the southeastern part of Sweden. The decision to build the new road through this important aquifer has been strongly questioned by the municipality and the public because the value of the water resources in Nybroåsen was not taken into consideration during the first phase of the decision-making process. Multicriteria decision aid (MCDA) is applied as a tool to structure and analyze the four road alternatives proposed in the environmental impact assessment for the above conflict. The alternatives, including the existing road, are in conflict with water resources, agriculture, natural, and cultural values. The impacts on the water resources have been predicted by a two-dimensional physically based time-variant flow and solute groundwater model, which has been calibrated and evaluated. The MCDA illustrates how it can be used to identify (1) actors and their concerns, (2) ranking of alternative road scenarios according to actors preferences, and (3) coalition groups of actors, i.e., groups that have similar views with regard to the road alternatives. The analysis shows that the two best compromise solutions obtained in the MCDA were unacceptable by the strongest coalition group, showing that either the family of criteria was not well chosen or new alternatives should have been developed.     

Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City,Iran, by Using an Artificial Neural Network

Particulate matter (PM), along with other air pollutants, pose serious hazards to human health. The Artificial Neural Network (ANN) is a branch of artificial intelligence that has an ability to make accurate predictions. In this article, the authors describe such methods and how historical data on air quality, moisture, wind velocity, and temperature in Shahr‐e Ray City, located at the southern tip of Tehran, was used to train an ANN to provide accurate predictions of PM concentrations. The availability of such predictions can offer significant assistance to those who are working to reduce air pollution.     

A multi-criteria framework to assess the sustainability of renewable energy development in the Alps

A multi-criteria analysis (MCA) was implemented to assess the best solutions for enhancing the production of renewable energy in the Alps. A set of criteria were selected based on the impacts of four renewable energy sources (forest biomass, hydropower, ground solar photovoltaic and wind power) on the three spheres of sustainability (environmental, social and economic). Three different scenarios are presented, each with a different set of weights for the criteria: the first scenario considers equally all three aspects of sustainability; the second scenario foresees an environmentally-oriented perspective, while the third scenario is more focused on the socio-economic aspects related to the development of renewable energy. Results show that forest biomass and hydropower seem to be the most viable solutions for enhancing the share of renewable energy in the Alps. Ground solar photovoltaic and wind power, on the other hand, seem to be less attractive alternatives due to their high impacts on land use.     

Formulating an ecosystem approach to environmental protection

The U.S. Environmental Protection Agency (EPA) has embraced a new strategy of environmental protection that is place-driven rather than program-driven. This new approach focuses on the protection of entire ecosystems. To develop an effective strategy of ecosystem protection, however, EPA will need to: (1) determine how to define and delineate ecosystems and (2) categorize threats to individual ecosystems and priority rank ecosystems at risk. Current definitions of ecosystem in use at EPA are inadequate for meaningful use in a management or regulatory context. A landscape-based definition that describes an ecosystem as a volumetric unit delineated by climatic and landscape features is suggested. Following this definition, ecosystems are organized hierarchically, from megaecosystems, which exist on a continental scale (e.g., Great Lakes), to small local ecosystems.Threats to ecosystems can generally be categorized as: (1) ecosystem degradation (occurs mainly through pollution) (2) ecosystem alteration (physical changes such as water diversion), and (3) ecosystem removal (e.g., conversion of wetlands or forest to urban or agricultural lands). Level of threat (i.e., how imminent), and distance from desired future condition are also important in evaluating threats to ecosystems. Category of threat, level of threat, and distance from desired future condition can be combined into a three-dimensional ranking system for ecosystems at risk. The purpose of the proposed ranking system is to suggest a preliminary framework for agencies such as EPA to prioritize responses to ecosystems at risk.     

Using Multicriteria Methods in Environmental Planning and Management

In environmental planning and decision processes several alternatives are analyzed in terms of multiple noncommensurate criteria, and many different stakeholders with conflicting preferences are involved. Based on our experience in real-life applications, we discuss how multicriteria decision aid (MCDA) methods can be used successfully in such processes. MCDA methods support these processes by providing a framework for collecting, storing, and processing all relevant information, thus making the decision process traceable and transparent. It is therefore possible to understand and explain why, under several conflicting preferences, a particular decision was made. The MCDA framework also makes the requirements for new information explicit, thus supporting the allocation of resources for the process.     

Socio-economic, environmental, and governance impacts of illegal logging

This article examines the main impacts of the illegal logging activities with emphasis on the most important timber production regions. Although the discussion is focused on the environmental, socio-economic and governance impacts, it must be stressed that the term forest governance is the umbrella where the economy, the environmental values, and welfare of forest-dependent communities are sheltered. Considering the effects/impacts of the illegal logging as a whole, we can summarize the following negative key points: (1) Degradation of the most valuable forest stands threatening biodiversity, including rare and endangered species. (2) Increase in soil erosion and landslides. Increase in CO₂ release and climatic changes. (3) Human rights abuses and disrespect of the basic needs of local communities and their culture. (4) Corruption, crime, coercion, and money laundering. (5) Reduction of royalties, taxes, and other charges paid by logging companies to the producer States. (6) Depreciation of legal activities due to the unfair concurrence. Despite multiple efforts, the results in the combat of illegal logging are far from satisfactory. Increase the certification area in parallel with an accurate control, devolve the state land to the ancient local owners, increase the cooperation between civil society and forest authorities, and finally strongly improve forest governance, particularly anti-money laundering laws, are crucial aspects in the combat of illegal logging. Furthermore, governments and businesses must implement the triple bottom line concept in order to reach sustainability.     

Biomass systems and environmental degradation in an Himalayan village

Summary Detailed analyses of the utilisation of the natural forest and its forest products by the community of a small Indian Himalayan village, reveal various interactions between the forest, the people, their agricultural systems and their methods of animal husbandry. Biomass flow systems have been identified and processes of forest and environmental degradation described.     

The Decision Analysis Interview Approach in the Collaborative Management of a Large Regulated Water Course

There are always conflicting goals in the management of large water courses. However, by involving stakeholders actively in the planning and decision-making processes, it is possible to work together toward commonly acceptable solutions. In this article, we describe how we applied interactive multicriteria decision analysis (MCDA) in a collaborative process which aimed at an ecologically, socially, and economically sustainable water course regulation policy. The stakeholders' opinions about the alternative regulation schemes and the relative importance of their impacts were elicited with the HIPRE software. Altogether, 20 personal interactive decision analysis interviews (DAIs) were carried out with the stakeholders. Our experience suggests that the DAIs can considerably improve the quality and efficiency of the collaborative planning process. By improving communication and understanding of the decision situation in the steering group, the approach helped to develop a consensus solution in a case having strong conflicts of interest. In order to gain the full benefits of the MCDA approach, interactive preference elicitation is vital. It is also essential to integrate the approach tightly into the planning and decision-making process. The project's home pages are available to the public at http://www.paijanne.hut.fi/.     

Multicriteria evaluation of simulated logging scenarios in a tropical rain forest

Forest growth models are useful tools for investigating the long-term impacts of logging. In this paper, the results of the rain forest growth model FORMIND were assessed by a multicriteria decision analysis. The main processes covered by FORMIND include tree growth, mortality, regeneration and competition. Tree growth is calculated based on a carbon balance approach. Trees compete for light and space; dying large trees fall down and create gaps in the forest. Sixty-four different logging scenarios for an initially undisturbed forest stand at Deramakot (Malaysia) were simulated. The scenarios differ regarding the logging cycle, logging method, cutting limit and logging intensity. We characterise the impacts with four criteria describing the yield, canopy opening and changes in species composition. Multicriteria decision analysis was used for the first time to evaluate the scenarios and identify the efficient ones. Our results plainly show that reduced-impact logging scenarios are more 'efficient' than the others, since in these scenarios forest damage is minimised without significantly reducing yield. Nevertheless, there is a trade-off between yield and achieving a desired ecological state of logged forest; the ecological state of the logged forests can only be improved by reducing yields and enlarging the logging cycles. Our study also demonstrates that high cutting limits or low logging intensities cannot compensate for the high level of damage caused by conventional logging techniques.     

A note on the use of the analytic hierarchy process for environmental impact assessment

Environmental impact assessment (EIA) is an intrinsically complex multi-dimensional process, involving multiple criteria and multiple actors. Multi-criteria methods can serve as useful decision aids for carrying out the EIA. This paper proposes the use of a multi-criteria technique, namely the analytic hierarchy process (AHP), for the purpose. AHP has the flexibility to combine quantitative and qualitative factors, to handle different groups of actors, to combine the opinions expressed by many experts, and can help in stakeholder analysis. The main shortcomings of AHP and some modifications to it to overcome the shortcomings are briefly described. Finally, the use of AHP is illustrated for a case study involving socio-economic impact assessment. In this case study, AHP has been used for capturing the perceptions of stakeholders on the relative severity of different socio-economic impacts, which will help the authorities in prioritizing their environmental management plan, and can also help in allocating the budget available for mitigating adverse socio-economic impacts.     

Uncertainty modelling in multi-criteria analysis of water safety measures

Water utilities must assess risks and make decisions on safety measures in order to obtain a safe and sustainable drinking water supply. The World Health Organization emphasises preparation of water safety plans, in which risk ranking by means of risk matrices with discretised probability and consequence scales is commonly used. Risk ranking enables prioritisation of risks, but there is currently no common and structured way of performing uncertainty analysis and using risk ranking for evaluating and comparing water safety measures. To enable a proper prioritisation of safety measures and an efficient use of available resources for risk reduction, two alternative models linking risk ranking and multi-criteria decision analysis (MCDA) are presented and evaluated. The two models specifically enable uncertainty modelling in MCDA, and they differ in terms of how uncertainties in risk levels are considered. The need of formal handling of risk and uncertainty in MCDA is emphasised in the literature, and the suggested models provide innovations that are not dependent on the application domain. In the case study application presented here, possible safety measures are evaluated based on the benefit of estimated risk reduction, the cost of implementation and the probability of not achieving an acceptable risk level. Additional criteria such as environmental impact and consumer trust may also be included when applying the models. The case study shows how safety measures can be ranked based on preference scores or cost-effectiveness and how measures not reducing the risk enough can be identified and disqualified. Furthermore, the probability of each safety measure being ranked highest can be calculated. The two models provide a stepwise procedure for prioritising safety measures and enable a formalised handling of uncertainties in input data and results.     

Degradation of methyl isothiocyanate and chloropicrin in forest nursery soils

Recent studies have observed enhanced degradation of methyl isothiocyanate (MITC) from repeated fumigation in agricultural soils. Little is known about fumigant degradation in forest and nursery soils. This study was conducted to determine degradation rates of MITC and chloropicrin (CP) in two forest soils and the impacts of nursery management on degradation of MITC and CP. The half-life values of MITC and CP were evaluated in the laboratory under isothermal conditions (22 +/- 2 degrees C). Three rates representing 0.5x, 1x, and 2x field application rates for each fumigant were used in laboratory incubations. Effect of microbial degradation was determined by conducting incubations with both fresh and sterilized soils. Soil moisture effects were also studied. There was no difference in MITC or CP degradation between fumigated and nonfumigated forest nursery soils. Soil sterilization and high soil moisture content (15% by wt.) reduced MITC and CP degradation. The degradation rates of MITC and CP varied with factors such as nursery history, fumigant application rates, and freshness of tested soils.     

Renewable energy consumption and its environmental impacts: A meta-regression analysis

This study involves a meta-analytical review of several articles, using the closest assumptions for the independent variable (renewable energy consumption), to determine the relationship with its environmental impacts. Furthermore, Sustainable Development Goal (SDG) in 2015 pays attention to related problems in both developing and developed countries. The effects of carbon emissions, natural resources, renewable energy consumption, and non-renewable energy on the environment are also reviewed. Following the separation of studies, the closest assumption was observed for the independent variable (energy source applications) of renewable energy. The meta-analysis was obtained using OpenMEE and JASP, and the results show the observed outcome, which was expressed in percentage. Based on the analysis, the Renewable Energy model has a value of 0.15, with the largest forest plots of 0.73, 0.69, and 0.80, recorded for studies 13, 19, and 23, respectively, while the least forest plots were recorded for studies 6, 11, 12, 15, 20, 22, 24, 25, 26, and 28. Therefore, renewable energy consumption, specifically in the mining industry, has the capacity to influence global warming. The respective industry has a unique influence on the processes that require renewable energy, for instance, the coal mining industry produces a smaller forest plot value, compared to oil and gas, or other mineral industries.