Related Work on Strategic Environmental Assessment and Optimization

Policy making is a domain where the scope of decisions is broad and the impact can affect large parts of the society. At the same time, influencing factors like environmental, economic, and social aspects have to be considered. In recent years environmental and social impacts gained more importance so that strategic environmental assessment (SEA) was enforced by law in many countries. The goal of SEA is to analyze impacts on the environment caused by political decisions. This results in a complex set of relationships that demands a scientific analysis. Multiple authors from the field of SEA claim that the methods to analyze environmental impacts are not well integrated into the policy making cycle [Fis07] [The12] [DCS05]. One reason for this is the complexity of SEA systems that usually depend on many influencing ‘dimensions’. The authors suggest that SEA concepts have to be presented more clearly and robust to the decision makers.

One possible way to address multidimensional decision problems is mathematical optimization. Es-tablished models and algorithms that support multidimensional problems exist, but they have to be transferred to the policy making process. Recent attempts in the optimization domain have created models which support multidimensional decision making with integrated environmental assessment, but most of them are lacking a visual interface to make the results accessible to non-experts [GMHO12]

[YTGS12] [LSKP10]. SEA could provide even more value to the decision making process if decision makers could directly work with the models.

In our approach, we introduce a visual interface to multidimensional optimization models. Goal of this interface is to reduce the complexity of the underlying optimization models in order to provide non-IT-experts an intuitive access to advanced analysis functionality. The user can interactively adjust input parameters, and analyze the resulting alternative solutions. As a use case our visual interface is coupled with a SEA model designed for creating regional energy plans. The underlying optimization model concerns environmental, social and economic impacts of these energy plans on a regional level.

The results of this optimization are interpreted by decision makers as well as domain experts, and may conclude in policy options to be considered in the policy making process. The visual interface is also designed to overcome knowledge gaps between different stakeholders. With our approach collaboration between decision makers and domain experts is facilitated due to a common information base provided by the visualization. The visual-interactive design makes use of an optimization model to compute solutions and provides clearly structured information about environmental impacts as requested by [The12].

9.2. Related Work on Strategic Environmental Assessment and Optimization

We review related work on strategic environmental assessment (SEA) at the policy level, and optimiza-tion in general as the applicaoptimiza-tion domains of our approach.

Strategic environmental assessment (SEA) is a proactive approach for integrating environmental concerns into early phases of decision making. The target is to anticipate and prevent environmen-tal, economic and social damage by predicting the impacts. Especially in the policy making process

where contrary options have to be evaluated SEA concepts can be applied profitably. The first SEA system was already established in 1969 by The US National Environmental Policy Act (NEPA). With the first SEA system applied by the World Bank in 1989 the acknowledgment rose, and more coun-tries started to make use of SEA [Sad05] [DCS05]. Nowadays, SEA is an approved methodology and is used in many countries [The12]. In [Fis07], Fischer postulates three meanings of SEA: a) a sys-tematic decision support process, b) an evidence-based instrument for scientific assistance in policy making, and c) a framework for the better consideration of alternative policy options for sustainable development. Therivel additionally recommends an increasing participation and collaboration of mul-tiple domains in the policy making process. The target of SEA should be to deliver robust data and clearly presented information [The12]. The LEAP system implements an SEA approach in the field of energy planning [Hea12]. It provides functions to analyze energy consumption, production and re-source extraction while monitoring the resulting greenhouse gas emissions. Further approaches that are concerned with energy efficiency can be found in the survey of Markovic et al. [MCM11]. A further work to be mentioned is the visual-interactive tool ComVis created by Matkovic et al. to assist the engine design process by optimizing diesel injection [MGJ^∗10]. The optimal parameter set up for an engine construction is analyzed via visual-interactive simulation and optimization systems.

Optimization models can describe complex decision making problems. To make the results under-standable for non-domain-experts visualization can help as suggested in Jones’ work [Jon96]. Multiple approaches that consider environmental, economic and social impacts were submitted by the domain of optimization, since multi-objective optimization models are able to support these problems [GGG10]

[YTGS12] [Aga13]. Further authors have combined the policy making process with optimization and environmental assessment like You et al. for optimizing bio-fuel supply chains [YTGS12], or Lim et al. for water infrastructure optimization [LSKP10]. They have committed models that are able to solve multidimensional decision making problems. Yet, they lack visual interfaces that could enable the involvement of decision makers into the process.

9.3. Domain and Problem Characterization

In our approach we introduce a visual interface to provide access to multidimensional optimization models for SEA. In our use case the optimization model tackles the problem of defining an optimal energy plan on a regional level. This complex mathematical model is difficult to understand by non-modeling-experts like policy makers. To address this challenge, we connect visualizations to the model which facilitates the access for policy makers. In the following, we first briefly describe the model with its variables, dependencies, possible target functions, and constraints. Then, we summarize the user requirements coming from policy makers and domain experts. And finally, we present the visual designs to enable the visual access to the multidimensional optimization problem.

9.4. Visual Analytics Design

9.3.1. Modeling a Regional Energy Plan

In this section, we describe our collaborative approach with the goal to find an optimal energy plan on a regional level. The resulting energy plan consists of a set of energy sources (primary activities), that each produces a specific amount of energy. The plan also includes secondary activities needed for the installation of the respective energy sources (e.g., aerial power lines, dams, etc.). Multiple aspects have to be considered in this scenario. The government has only a limited budget to incentivize the construction of new plants. Still, a defined minimum of energy has to be produced. There are multiple types of energy sources that can be installed. Some are more efficient, others are more sustainable.

Moreover, each region has geographical characteristics that restrict some types of energy sources. In addition, governmental laws have to be observed. Often, they aim at the protection of nature and prohibit the extensive use of polluting energy plants. This is also a demand of the society, which is directly affected by the impact of new policies. In summary, considering all dependencies and finding a solution that satisfies all constraints results in a multidimensional decision problem.

In our approach, we made use of an optimization model to address this multidimensional decision problem. More specifically, a linear optimization model is used, designed by modeling experts, which can be reviewed in [GMHO12]. Please note, that our visual designs only consider input parameters and output data of the optimization model. Hence, it can be easily adapted to other linear, and even non-linear optimization problems. A linear optimization problem can be mathematically described as max(~c^T~x|A~x≤~b, ~x≥0). In our case the vector~xto be optimized consists of the amount of energy to be produced by each of the energy sources included in the model.~c^T~xdefines the target function to be maximized. Thereby,~cencodes the target of the optimization problem, e.g., overall energy produced, overall cost, impact on an environmental receptor, etc. A~x≤bencodes the constraints on the problem.

Thereby, similar to~c, each row of the matrix A together with the boundary value comprised by~b describes a constraint. After the definition of the optimization model, an optimal solution vector~xcan be calculated, if a solution exists. This vector comprises the optimal amount of energy to be produced by each energy source.

9.3.2. User Requirements

At the beginning of our approach, requirements were identified with the user groups of policy mak-ers, domain experts and modeling experts. Moreover, a questionnaire was sent to the potential user groups to confirm the identified requirements, and determine further refinements. As a result of this requirements analysis, the final requirements for this approach are shown in Table9.1.

9.4. Visual Analytics Design

Based on the results of the requirements analysis phase, we present a web-based system for the visual access to multidimensional optimization models in the application field of strategic environmental as-sessment. The various input parameters of the model can be defined in a visual-interactive manner.

Req. Description Challenge R1 Visual definition of target function and constraints creation

R2 Analysis of individual solution analysis

R3 Comparison of multiple (possibly pre-calculated) solutions comparison R4 Consideration of environmental, economic and social impacts analysis

Table 9.1.:Functional requirements for the visual-interactive DSS.

This encapsulation of the optimization model itself via visual interfaces helps to reduce the complex-ity of the input space. The output space of the model is represented in a two-stage design. Firstly, a result visualization gains insight into the output data of the model. Secondly, an interface to visually compare results of different parameterizations helps to determine optimal input parameter setups. In the following, the visual designs of the web-system are described.

9.4.1. The Input Interface

makes available all possible degrees of freedom of the optimization model (see Figure9.2). The user is enabled to define target function and constraints to specify the optimization problem (seeR1). The visualization provides three sections to address these tasks. In the upper part the target variable to be maximized or minimized can be chosen. Below constraints on the energy sources can be specified.

Please note, that in our use case as a maximum value for each energy source the available regional capacity of each respective source is set. Hence, the user can refine these constraints within the range of 0 and the maximal regional capacity. Moreover, additional constraints on the environmental, social, and economic impacts can be set. Finally, the specified parameters can be labeled as a plan, and the optimal solution can be calculated.

9.4.2. The Optimized Plan View

visualizes the output data of the calculated energy plan based on the inputs defined in the Input Interface (see Figure9.3). This addresses requirement R2. It gives an overview of the amount of energy (in megawatt or kilotons of oil equivalent) and the costs to be produced by the plan (top left). Additionally, environmental, social and economic impacts are displayed (top right), which addresses requirementR4. Moreover, the secondary activities needed for the installation of the energy sources are depicted (top middle and bottom).

The information in this view consists of nominal and quantitative data. Hence, we chose bar charts as visualization technique, as proposed in the literature [Few09]. Moreover, this technique is easy to understand for non-IT-experts. A normalized stacked bar chart depicts the percentages of secondary activities needed for the installation of primary activities (energy sources). The impacts are depicted via a heatmap to save display space. The values of the different impact types cannot be compared

9.4. Visual Analytics Design

Figure 9.2.:The input interface for specifying target function (e.g., maximize energy), constraints on budget and energy source capacities (e.g., biomass plants capacities), and constraints on environmental impacts caused by the energy plan (e.g., air quality).

Figure 9.3.: The optimized plan view visualizes the output data of the optimization. It depicts the quan-tity and costs of energy to be produced per source, impacts on the environment, and additional secondary activities needed for the realization of the plan.

Figure 9.4.:The comparison of plans view enables the user to compare plans calculated with different input parameters. Here, four different plans are depicted. The user can compare the overall energy and costs to be produced by each plan, detailed information about the energy source mix, and impacts on the environment.

because they are measured in different units. If requested, more detail on the impacts can be viewed in the Impacts View, an additional matrix heatmap visualization mapping secondary activities (rows) on impacts (columns). For the visualizations we used an evaluated categorical color map [HB03] to depict the nominal data labeling the distinct energy sources. A diverging color map is used to depict the quantitative impact values in the heatmap; negative (red), neutral (white), or positive (green).

9.4.3. The Compare View

visualizes a set of energy plans the user wants to compare (see Figure9.4). This view covers functional requirementR3. To compare the different facets of the plans each variable is visualized separately. The top layer allows the user to get a fast overview of the compared plans by presenting the overall energy, and the overall costs produced by each plan via bar charts. The middle layer of the visualization splits the energy produced and the costs into the energy sources and displays them as grouped bar charts.

The heat map, also presented in the Optimized Plan View, shows the different impacts on environment, society and economy, and therefore addresses requirementR4.