- Email: [email protected]
Holistic Benchmarking of the Bio-economy in Protected Landscape Areas
Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 43 (2015) 118 – 126
ICTE in Regional Development, December 2014, Valmiera, Latvia
Holistic Benchmarking of the Bio-economy in Protected Landscape Areas Ginta Majorea,b*, Valdis Zakisa, Mairita Zakea, Egils Gintersa,b, Krisjanis Zakisa, Andris Fjodorovsa a
Faculty of Engineering, Vidzeme University of Applied Sciences, 4 Cesu Street, Valmiera, Latvia LV-4200 Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences, 4 Cesu Street, Valmiera, Latvia LV-4200
b
Abstract Mobile technologies are significant for sustainability assessment processes in the bio-economy. The project which produced this paper was designed to examine those factors which needed to be included in the study of the significant and complex elements in order to analyse the environmental impact on the bio-economy within protected landscape areas. The study also focused on drawing conclusions on those areas which needed to change to preserve the sustainable development of the bio-economy. The methods used in the project included extracting features of protected landscape areas from the Google Earth map studying them and applying data collected to the specific protected areas. Much of the physical focus was centered on Vestiena, Latvia, its environment and significant physical features. The aim of this paper was to discuss a holistic approach to the analysis of an ecological footprint in the protected landscape areas. The outcomes of this paper were: (1) an analysis of the simulation model; (2) development of a mobile solution based on Google Earth services; (3) creation of a holistic benchmarking factors influencing sustainable development of protected landscape area. This paper also discussed the features included in the current lifecycle of landscape and regional development based on economic metric tools and their application to planning. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences. Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences Keywords: Map benchmarking; Life cycle modeling; System dynamics simulation; Bio-economy; Environmental sustainability
* Corresponding author. E-mail address: [email protected]
1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences doi:10.1016/j.procs.2014.12.016
Ginta Majore et al. / Procedia Computer Science 43 (2015) 118 – 126
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1. Introduction The Europe 2020 Strategy, which takes full account of the economic, social and environmental dimensions of development, has helped to focus policy-makers’ attention on methods of combining economic development and the well-being of people with the fair distribution and preservation of scarce resources. The bio-economy encompasses the production of renewable biological resources and their conversion into food, bio-based products and bioenergy through the innovative and effective technologies provided by industrial biotechnology. It is already a reality and one that offers great opportunities and solutions to a growing number of major societal, environmental and economic challenges, including climate change mitigation, energy and food security and resource efficiency1. Measuring GDP together with environmental and social indicators becomes a more reliable assessment of local, regional and national progress; it also reflects the development of consumption patterns. On the other hand, the scoreboard of key social indicators allows for improved and earlier identification of major social problems which can hamper economic growth and well-being. Protected landscapea areas are currently not included in development or bio-economic topics. Indicators of resource currently use material and energy flow accounts, emission data and the ecological footprint to inform society regarding their performance by evaluating resource use efficiency and the effectiveness of sustainability policies. One of objectives of this research paper is to develop a mobile Internet prevalence model that would let users to determine levels of ecological footprint indicators and elaborate the simulation models of environmental sustainability. 2. The collection of data 2.1. Data measurement In this study the data can be developed using data from specific areas identified on Google Earth maps and which was then searched more closely for the existence of these natural phenomenon sought – rivers, roads, hedges etc. specific natural trends. 2.2. Data preparation The linear regression method was used to estimate secondary data derivatives from biological resource measurements. As an example: a) the tree diameter inside the bark, considering that the bark are not often used; b) scaling the distances between forest patches; c) water surface protection zone widths according to legislation etc. 2.3. Landscape metrics Landscape level metrics, which describe the landscape composition, while others characterise landscape configuration, quantify data geostatistically. Landscape composition and configuration can affect ecological processes and impact independently and interactively. It was especially important that landscape pattern was quantified to avoid metrics which are partially or completely redundant, e.g to quantify a similar or identical aspect of landscape pattern. For example, at the landscape level, patch density (PD) and mean patch size (MPS) will perfectly represent the same information. These redundant metrics are used as alternative methods of representing the same information. As a number, metrics exhibit erratic and/or unstable behavior at extreme conditions and this accentuates the need to apply them intelligently. The main subtask was to succeed in the application of diverse and
a
Landscape (ecological and management prospective) is large (1000-10 000 ha) spatially heterogenic areas with mosaic of interacting ecosystems and populations of many species
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reliable metrics to geostatistical quantification of protected/scenic landscapes in the absence of environmentally based and socially based techniques. 3. Model development 3.1. Software prototype design for natural resource use and sustainability assessment of households in protected landscape areas Specific software (see Fig. 1) is intended to help evaluate and analyse protected landscape areas and their natural resource use and sustainability. For example, it will show if building a house in a protected landscape area will have environmental consequences for the area in the long term. The software is based on an interactive map, containing tools and data needed for the analysis. Figure 1 shows the blueprint for a proposed software prototype. The main options are as follows: x Drawing tools which contain area setting and variable definition functions and enables the marking of interested areas on the map, this could include building, a forest, a lake etc. and also provide necessary data for evaluation; x Search tools provide a method navigating between identified item or locations; x Analysis tools provide an evaluation function of selected item or area and provide results concerning the natural resources.
Fig. 1. Software prototype working blueprint.
3.2. Scenic / aesthetic landscape metrics The naturalness and landscape where accepted as assessment criteria along with pre-selected indicators. The naturalness criterion was chosen based on the findings of Ode et al. (2009)2: the gradual indication of the degree of disturbance made by human beings was expressed by Shape Index (SHAPE). The forested landscape diversity criteria was chosen based on de Groot et al. (2010)3 and Herbst et al. (2009)4, to highlight the relevance of structural diversity in description of scenic beauty. Shannon’s diversity index (SHDI) and patch density (PD) were calculated as landscape diversity measure. The Diversity Index (SHDI), the Shape Index (SHAPE) and Patch Density (PD)
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were used as indicators. These were earlier validated in a model landscape region in Germany 5, which is approximately to Latvia. The land classes were taken from CLLC 2006. For the assessment of aesthetics values ahermeobe, oligo- and mesohemerobe classes were taken into account (see Table 1). Table 1. Clustering of the land cover classes by hemeroby groups6 CLC 2006-Class
Degree of hemerobya
Human impact
-
Ahermeobe
None
Natural grasslands, sparsely vegetated areas, moors and heathland, transitional woodland-shrub
Oligohemerobe
Limited removal of wood, pastoralism, emission through air and water
Land principally occupied by agriculture, with significant areas of natural vegetation; broad-leaved forest; Coniferous forest; mixed forest
Mesohemerobe
Clearing and occasional ploughing, clear cut, occasional slight fertilization
The stepwise aggregation process for adapting the landscape metrics’ values to the qualitative assessment routines in GISCAME7 was used. The potential of a landscape is expressed by value points7 on a scale from 0 - no or least provision - to 100 - the highest provision. The landscape metrics-based calculation results express the additional impact of landscape patterns on aesthetics. The final result is obtained by consolidating both value point7. The comparison of the results of the objective approach - landscape metrics and the subjective approach - visual assessment - was not the aim of this paper. 3.3. The ecological footprint model development In the world today, humanity has already exceeded planetary limits and ecological assets are becoming more critical. Each country has its own ecological risk profile while Latvia is running ecological deficit. The carbon footprint of Latvia is two-and-a-half times its natural resources and remains more than double the world average per person, 1.3 and global hectares per person, respectively. Latvia has greater ecological reserves than others, but it does not necessarily mean that it manage its assets well8. Its footprint is smaller than its own biological capacity, but it depend heavily on fossil resources from Russia. Latvia is under increasing ecological pressure. The Ecological Footprint indicator is used as resource accounting tool that measures how much bioproductive land and water is available. To characterise the sustainable development of a protected landscape area the Ecological Footprint indicator has been used to inform many different audiences and it is portrayed as a powerful educational and prognostic tool; it can facilitate the visualisation and comprehension of human demand on biological resources by expressing the dominant component impact as a proportion of CO2 emissions/ absorption. The ecological Footprint was initially developed and described in the 1990s by Rees and Wackernagel9, 10, 11. It is an estimate of the proportion of the planetary biological productivity and assimilative capacity effectively appropriated by the consumption of the given population or activity over a specified time period - usually a year. In the present paper energy, arable crops, grasslands, forests, water and other resources as evaluative categories have been classified as direct material production and consumption12. Basal area increment modelling is used to predict growth from a potential biological growth function multiplied by a modifier function 13. The Basal Area (BA) term is used as the specific cross-sectional area for each specific above ground biomass-based product. It is a conservative estimate of total product volume/ weight. The factors of biological resources and the respective equivalent factors are used to convert the actual physical area into global hectares or CO 2 equivalents.
a
anthropogenic landscape modification
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3.4. Human development The need for energy efficiency and supply constraints is dominant in the world of today. It has been demonstrated that developed countries with low per capita energy consumption have a high score in the Human Development Index (HDI). The HDI - 0.810 in 2013 ranked Latvia among those countries with a very high human development level14. 3.5. The life cycle benchmark assessment model The environmental simulation model evaluates the environmental impact of activities in a residential environment which can be assessed at different levels which have the following inputs in to consideration: weather external temperature, the sun radiation and precipitation - energy consumed in households - fuel for heating, lighting, business and transport, - energy produced in households by biologically productive land a - such as forest, meadows and alternative energy sources, social such as human, family, city and maintenance and replacement development. 3.5.1. Systems dynamics The systems dynamic simulation model has been created to simplify complex real-world problems for the Environmental Impact and Sustainability Assessment of Residential Development in Protected Areas. A model needs to represent clear understanding of the problems and their interdependence within the real-world system. A system dynamic model is one way to do this, and describes the inter-relation between the subsystems in the system, and also between the components of the subsystems involving feedback interactions. System dynamic models have been recognized as contributing a great deal to the improvement of the decision-making process in strategic planning through policy analysis by allowing the simulation of policy scenarios. Using a system dynamics model, behaviour both in character and in nature of the system can be easily understood, and it is possible to explore all possible development options through the evaluation of the behaviour of the model in given particular scenarios 15. System dynamics is concerned with the significant behavior of a system or a macroscopic view. It helps individuals to understand the aggregate operations of system on a macro-scale. It is very useful for cutting away unnecessary detail and focusing on what is truly important in a model16. The system dynamics method was created by Professor Forrester of Massachusetts Institute of Technology in the mid-1950s17. After decades of development and improvement, the Systemic Dynamics Model has been widely used in the study of the economy, society, ecology and many other complex systems18, 19, 20, 21. The system dynamics approach has provided a theoretical and practical foundation for modelling complex systems in a learning environment so it can be viewed as an effective approach through which different processes can be examined and different scenarios tested from a system perspective. The purpose of the system dynamics modelling approach is to obtain an understanding of, and insights into, system relationships and the search for alternative polices to improve the situation.22 3.5.2. A comparison of some of the existing system dynamics simulation tools The table below presents some general information regarding four system dynamics modeling environments. A brief description of particular simulation software is given in the Table 2:
a
Biologically productive land is land and water (both marine and inland) area that supports significant photosynthetic activity and biomass accumulation that can be used by humans
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Ginta Majore et al. / Procedia Computer Science 43 (2015) 118 – 126 Table 2. Brief comparison of some of the existing system dynamics simulation tools23 Name
Company URL
Demo/run time
Platform
Extendable
Userfriendliness
Learning curve
Comments
STELLA
High Performance
Runtime
Mac/Win
No
5
5
Lots of users. Most used in academic and business
Demo
Win
Somewhat
5
4
Growing fast Weboriented
Demo
Win/Mac
No
5
5
Inexpensive
CS
Runtime
Win
No
5
5
http://www.cherwell.com
Demo
Systems http://www.hps-inc.com POWERSIM
Powersim http://www.powersim.com
VENSIM
Ventana http://www.vensim.com
MODEL MAKER
x POWERSIM is an integrated environment for creating and running simulation models. It uses the block-oriented graphical modelling language taken from the system dynamics method to model a system. The tool uses presentation items such as graphs and tables and has linking capabilities;24, 25, 26 x VENSIM - originally - was developed as a tool for running STELLA models more effectively. It is now a modelling package in its own right, causal loop and system dynamics elements;27 x MODELMAKER is a visual simulation modelling package designed for scientists and engineers28. Models can be structured hierarchically with the definition of sub-models. The package offers – compared with the other systems listed in this section – a wide functionality concerning the analysis of the defined model. Monte Carlo analysis, sensitivity analysis as well as parameter estimation and model optimisation can be calculated using a generic algorithm approach or by a grid search strategy27. 3.5.3. STELLA description STELLA is a modelling tool for building a dynamic simulation models by creating a pictorial diagram of a system and then assigning the appropriate values and mathematical functions to the system. The key objects of STELLA consist of the following four tools: Stocks, Flows, Converters and Connectors. STELLA offers a practical way to dynamically visualize and communicate how complex systems and ideas work in reality. 29 STELLA has been widely used in biological, ecological and environmental sciences 22. It was specifically designed to facilitate the modelling of non-linear, dynamic systems to enhance the learning through scenario testing and analysis. 3.5.4. Model structure in STELLA The purpose of the model in this study was to simulate environmental impact and sustainability assessment of residential development in protected areas. Sustainable assessment requires a long-term perspective, and the level of uncertainty in long-term prediction is high. The time horizon of the model is 50 years. This time horizon is long enough to mean that the future of the system is relatively independent of its initial conditions. The time frame of the calculations is one month. The model consists of eight main components including forest, meadow, swamp, arable land, dwelling house, commercial activities, barn and transport. In Figure 2 it is possible to view the commercial activities sub-model. The amount of CO2 in the commercial activities sub-model is simulated calculating annual CO2 amount for heating the building, usage of electricity and water. The type of heating as well as the percentage increment in commercial activity is taken into account in the calculation of CO2 emissions. The difference in temperature between the temperature in the house and outside is simulated in a separate model and is taken into account in the calculation of the CO2 heating emissions.
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Fig. 2. A commercial activity sub-model.
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4. Discussion 4.1. Landscape metrics The paper has focused on the application of a residential sustainability assessment platform to calculate balance between scenic landscape, above-ground production and human development values which provides information not only regarding the intensity of land, energy and human resource use at this moment by using mobile web based mapping service application. The protected landscape areas consist of land remarkable for original and diverse landscapes and outstanding beauty created to ensure the preservation of the environment appropriate to recreational activities for citizens and tourists. Our model region is situated in the middle of Latvia and is called the Vestiena area where seven European Union protected forest habitats based on the habitat directive 92/43/EEC and one marsh habitat within the protected landscape area of Vestiena. There are in 32 lakes, artificial water constructions - 4 in the protected landscape area Vestiena in the Madona district of Latvia. Many are completely different in their characteristics such as area – from 1 to 407 hectares and in depth – from 1 to 35 meters water composition and their nature and development potential. The significant scenic lakes can be easily reached with a forest mosaic and farmland locations along their banks. Landscape metrics-based analysis was applied on the Vestiena area specific spatial database, which was available on Google Earth through the mobile application. Through the use of relatively simple landscape metrics including areas, relative shares, numbers, coast lengths etc., the transparency and comprehensibility of the assessment and modelling can be reached. A set of landscape metrics indicators was calculated to analyse land use patterns in the selected the ecosystem areas. The naturalness criteria Shape Index (SHAPE) ranged from <1.29 to >1.46. The structural diversity description of scenic beauty according to Shannon’s Diversity Index (SHDI) ranged from <1.06 to >1.73) and patch density (PD) index ranges from <0.29 to >0.67 per km 2. The selected landscape metrics-based indexes express more effectively the relative benefit of differences in spatial boundaries of mosaic land cover. As a result of the fact that, historically, lake estates were built at least 100 m from the lake or in - off lake area, the lakes pollution load was insignificant, and supported the creation of the landscaping. New buildings on the banks of the lakes have a negative effect, both as a source of human waste, as well as the visual changes in the naturally surrounded landscape. Constructions as close as possible to the lake have contaminated the lake landscape not only because of pollution from human economic activity, but also because of the deprivation of the surrounding views created by slopes and floodplains and the countryside in general which has reduced the potential for public use. 4.2. The ecological footprint The major focus of the approach is on the assessment of productivity within biologically productive landscape areas and the impact of human activity on existing and/or proposed new residential development in or adjacent to a protected landscape. The approach adopted in this project can educate users such as: environmentalist, regional developers and planners to enhance their decision making processes and the quality of their decisions regarding sustainable environmental development. The life cycle analysis of the ecological footprint, including above-ground biomass-based product and energy production, the consumption of fossil energy resources, and human development values provide information not only regarding the intensity of the use of land, energy and human resource today, but also future benchmarking values of sustainable protected landscape areas development. 5. Conclusions The scale testing of Goggle Earth based landscape and estate metering and bio-economy life cycle assessment tools could feature the holistic benchmarking of the production of renewable biological resources and their conversion into food, biological products and bioenergy and even waste, e.g. the development of bio-economy to assist society, the economy and nature. Biomass-based product, alternative and fossil energy throughput, as well as ecological footprint related CO2 emissions have good correlation with each other among farmsteads in the protected landscape area, while the human development indicators are site specific. The increased interest in the bio-economy in the protected landscape areas needs to become more specific and measurable. Consequently in order to continue
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to growth sustainable development the major players need to take increased responsibility for their actions. These players include local residents, tourists, real-estate owners, recreation managers, farmers, industrialists and policy makers. A significant challenge for the future is related to the development of GIS joined with simulation task and based on a spatial database for a scenic landscape during its entire life cycle and the spatial action project. References 1. Innovating for Sustainable Growth: A Bioeconomy for Europe. Communication from the Commission to the European Parliament, the Council, the European Economic And Social Committee and the Committee of the Regions. European Commission. Retrieved: 10.10.2014, URL: http://ec.europa.eu/research/bioeconomy/pdf/official-strategy_en.pdf 2. Ode, Å., Fry, G. Tveit, M.S., Messager, P., Miller, D., 2009. Indicators of perceived naturalness as drivers of landscape preference. J. Environ. Plann. Manage. 90, 375–383. 3. De Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272. 4. Herbst, H., Förster, M., Kleinschmit, B., 2009. Contribution of landscape metrics to the assessment of scenic quality – the example of the landscape structure plan Havelland/Gemany. Landscape 10, 1–17 5. Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz Makeschin Assessment of landscape aesthetics—Validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecological Indicators, Volume 32, September 2013, Pages 222-231 6. Blume, H.P., Sukopp, H., 1976. Ökologische Bedeutung anthropogener Bodenveränderungen. Schr. Reihe Vegetationskunde 10, 74–89. 7. Koschke, L., Fürst, C., Frank, S., Makeschin, F., 2012. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning. Ecol. Indicat. 21, 54–66. 8. Europe 2007: Gross Domestic Product & Ecological Footprint 9. Rees, W.E., 1992. Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environment and Urbanization 4, 121–130. 10. Rees, W.E., Wackernagel, M., 1994. Ecological footprints and appropriated carrying capacity: measuring the natural capital requirements of the human economy. In: Jansson, A.M., Hammer, M., Folke, C., Costanza, R. (Eds.), Investing in Natural Capital: the Ecological Economics Approach to Sustainability. Island Press, Washington DC, pp. 362–390. 11. Wackernagel, M., Rees, W., 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers, Gabriola Island, BC. 12. R. Bleischwitz International economics of resource productivity–relevance, measurement, empirical trends, innovation, resource policies, Int. Econ. Policy, 7 (2010), pp. 227–244 13. Hasenauer H., Methodische aspekte bei der Evaluierung von Baummodellen Deutscher Verband forstlicher Versuchsanstalten Ertragenkunde. Beitrag zur Jahres tagung in Volpriehhausen, 19-21 May 1999, pp.45-53. 1999 14. Human Development Report 2014, http://hdr.undp.org/en/2014-report/ 15. Wiranatha A. and Smith P. 2000 A Conceptual Framework for a Dynamic Model for Regional Planning: Towards Sustainable Development for Bali, Indonesia. 1st International Conference on Systems Thinking in Management. 649-654 16. Insight Maker, 2014. Types of modeling. Available from: http://insightmaker.com/modeling 17. Forrester, J.W., 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review 36 (4), 37–66. 18. Chang, Y., Hong, F., Lee, M., 2008. A system dynamic based DSS for sustainable coral reef management in Kenting coastal zone, Taiwan. Ecological Modelling 211(1–2), 153–168. 19. Wang, Y., Zhang, X., 2001. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecological Modelling 140 (1–2), 141–162. 20. Lektauers, A., Trušiņš, J., Trušiņa, I. A Conceptual Framework for Dynamic Modeling of Sustainable Development for Local Government in Latvia. No: Proceedings of the 28th International Conference of the System Dynamics Society, Korejas republika, Seoul, 25.-29. jūlijs, 2010. Seoul: System Dynamics Society, 2010, 1.-14.lpp. ISBN 9781935056065. 21. Tao, Z., 2010. Scenarios of China’s oil consumption per capita (OCPC) using a hybrid Factor Decomposition-System Dynamics (SD) simulation. Energy 35 (1), 168–180. 22. T. Shi, R. Gill. Developing effective policies for the sustainable development of ecological agriculture in China: the case study of Jinshan County with a systems dynamics model. Ecological Economics 53 (2005) 223–246 23. Voinov, A., 1999. Simulation Modeling, Online Course. http://iee.umces.edu/AV/Simmod.html 24. Edelfeldt S. and Fritzson P. 2007 Evaluation and comparison of models and modelling tools simulating nitrogen processes in treatment wetlands. Simulation Modelling Practice and Theory, Elsevier, Volume 16, Issue 1, p.26-49 25. Imagine That. 2013. Overview of ExtendSim. http://www.extendsim.com/prods_overview.html 26. The MathWorks. 2014. Simulink. Simulation and Model-Based Design. http://se.mathworks.com/products/simulink/ 27. Seppelt R. Computer-Based Environmental Management. John Wiley & Sons, 2007. ISBN 3527609210 28. Walker, A. 1997. ModelMaker. Cherwell Scientific, Oxford, U.K 29. Isee System, 2006. Technical document for the iThink and STELLA software. http://www.iseesystems.com 30. Costanza et al., 2002, R. Costanza, A. Voinov, R. Boumans, T. Maxwell, F. Villa, H. Voinov, L. Wainger. Integrated ecological economic modeling of the Patuxent river watershed, Maryland. Ecological Monographs, 72 (2002), pp. 203–231
ScienceDirect Procedia Computer Science 43 (2015) 118 – 126
ICTE in Regional Development, December 2014, Valmiera, Latvia
Holistic Benchmarking of the Bio-economy in Protected Landscape Areas Ginta Majorea,b*, Valdis Zakisa, Mairita Zakea, Egils Gintersa,b, Krisjanis Zakisa, Andris Fjodorovsa a
Faculty of Engineering, Vidzeme University of Applied Sciences, 4 Cesu Street, Valmiera, Latvia LV-4200 Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences, 4 Cesu Street, Valmiera, Latvia LV-4200
b
Abstract Mobile technologies are significant for sustainability assessment processes in the bio-economy. The project which produced this paper was designed to examine those factors which needed to be included in the study of the significant and complex elements in order to analyse the environmental impact on the bio-economy within protected landscape areas. The study also focused on drawing conclusions on those areas which needed to change to preserve the sustainable development of the bio-economy. The methods used in the project included extracting features of protected landscape areas from the Google Earth map studying them and applying data collected to the specific protected areas. Much of the physical focus was centered on Vestiena, Latvia, its environment and significant physical features. The aim of this paper was to discuss a holistic approach to the analysis of an ecological footprint in the protected landscape areas. The outcomes of this paper were: (1) an analysis of the simulation model; (2) development of a mobile solution based on Google Earth services; (3) creation of a holistic benchmarking factors influencing sustainable development of protected landscape area. This paper also discussed the features included in the current lifecycle of landscape and regional development based on economic metric tools and their application to planning. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences. Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences Keywords: Map benchmarking; Life cycle modeling; System dynamics simulation; Bio-economy; Environmental sustainability
* Corresponding author. E-mail address: [email protected]
1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences doi:10.1016/j.procs.2014.12.016
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119
1. Introduction The Europe 2020 Strategy, which takes full account of the economic, social and environmental dimensions of development, has helped to focus policy-makers’ attention on methods of combining economic development and the well-being of people with the fair distribution and preservation of scarce resources. The bio-economy encompasses the production of renewable biological resources and their conversion into food, bio-based products and bioenergy through the innovative and effective technologies provided by industrial biotechnology. It is already a reality and one that offers great opportunities and solutions to a growing number of major societal, environmental and economic challenges, including climate change mitigation, energy and food security and resource efficiency1. Measuring GDP together with environmental and social indicators becomes a more reliable assessment of local, regional and national progress; it also reflects the development of consumption patterns. On the other hand, the scoreboard of key social indicators allows for improved and earlier identification of major social problems which can hamper economic growth and well-being. Protected landscapea areas are currently not included in development or bio-economic topics. Indicators of resource currently use material and energy flow accounts, emission data and the ecological footprint to inform society regarding their performance by evaluating resource use efficiency and the effectiveness of sustainability policies. One of objectives of this research paper is to develop a mobile Internet prevalence model that would let users to determine levels of ecological footprint indicators and elaborate the simulation models of environmental sustainability. 2. The collection of data 2.1. Data measurement In this study the data can be developed using data from specific areas identified on Google Earth maps and which was then searched more closely for the existence of these natural phenomenon sought – rivers, roads, hedges etc. specific natural trends. 2.2. Data preparation The linear regression method was used to estimate secondary data derivatives from biological resource measurements. As an example: a) the tree diameter inside the bark, considering that the bark are not often used; b) scaling the distances between forest patches; c) water surface protection zone widths according to legislation etc. 2.3. Landscape metrics Landscape level metrics, which describe the landscape composition, while others characterise landscape configuration, quantify data geostatistically. Landscape composition and configuration can affect ecological processes and impact independently and interactively. It was especially important that landscape pattern was quantified to avoid metrics which are partially or completely redundant, e.g to quantify a similar or identical aspect of landscape pattern. For example, at the landscape level, patch density (PD) and mean patch size (MPS) will perfectly represent the same information. These redundant metrics are used as alternative methods of representing the same information. As a number, metrics exhibit erratic and/or unstable behavior at extreme conditions and this accentuates the need to apply them intelligently. The main subtask was to succeed in the application of diverse and
a
Landscape (ecological and management prospective) is large (1000-10 000 ha) spatially heterogenic areas with mosaic of interacting ecosystems and populations of many species
120
Ginta Majore et al. / Procedia Computer Science 43 (2015) 118 – 126
reliable metrics to geostatistical quantification of protected/scenic landscapes in the absence of environmentally based and socially based techniques. 3. Model development 3.1. Software prototype design for natural resource use and sustainability assessment of households in protected landscape areas Specific software (see Fig. 1) is intended to help evaluate and analyse protected landscape areas and their natural resource use and sustainability. For example, it will show if building a house in a protected landscape area will have environmental consequences for the area in the long term. The software is based on an interactive map, containing tools and data needed for the analysis. Figure 1 shows the blueprint for a proposed software prototype. The main options are as follows: x Drawing tools which contain area setting and variable definition functions and enables the marking of interested areas on the map, this could include building, a forest, a lake etc. and also provide necessary data for evaluation; x Search tools provide a method navigating between identified item or locations; x Analysis tools provide an evaluation function of selected item or area and provide results concerning the natural resources.
Fig. 1. Software prototype working blueprint.
3.2. Scenic / aesthetic landscape metrics The naturalness and landscape where accepted as assessment criteria along with pre-selected indicators. The naturalness criterion was chosen based on the findings of Ode et al. (2009)2: the gradual indication of the degree of disturbance made by human beings was expressed by Shape Index (SHAPE). The forested landscape diversity criteria was chosen based on de Groot et al. (2010)3 and Herbst et al. (2009)4, to highlight the relevance of structural diversity in description of scenic beauty. Shannon’s diversity index (SHDI) and patch density (PD) were calculated as landscape diversity measure. The Diversity Index (SHDI), the Shape Index (SHAPE) and Patch Density (PD)
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were used as indicators. These were earlier validated in a model landscape region in Germany 5, which is approximately to Latvia. The land classes were taken from CLLC 2006. For the assessment of aesthetics values ahermeobe, oligo- and mesohemerobe classes were taken into account (see Table 1). Table 1. Clustering of the land cover classes by hemeroby groups6 CLC 2006-Class
Degree of hemerobya
Human impact
-
Ahermeobe
None
Natural grasslands, sparsely vegetated areas, moors and heathland, transitional woodland-shrub
Oligohemerobe
Limited removal of wood, pastoralism, emission through air and water
Land principally occupied by agriculture, with significant areas of natural vegetation; broad-leaved forest; Coniferous forest; mixed forest
Mesohemerobe
Clearing and occasional ploughing, clear cut, occasional slight fertilization
The stepwise aggregation process for adapting the landscape metrics’ values to the qualitative assessment routines in GISCAME7 was used. The potential of a landscape is expressed by value points7 on a scale from 0 - no or least provision - to 100 - the highest provision. The landscape metrics-based calculation results express the additional impact of landscape patterns on aesthetics. The final result is obtained by consolidating both value point7. The comparison of the results of the objective approach - landscape metrics and the subjective approach - visual assessment - was not the aim of this paper. 3.3. The ecological footprint model development In the world today, humanity has already exceeded planetary limits and ecological assets are becoming more critical. Each country has its own ecological risk profile while Latvia is running ecological deficit. The carbon footprint of Latvia is two-and-a-half times its natural resources and remains more than double the world average per person, 1.3 and global hectares per person, respectively. Latvia has greater ecological reserves than others, but it does not necessarily mean that it manage its assets well8. Its footprint is smaller than its own biological capacity, but it depend heavily on fossil resources from Russia. Latvia is under increasing ecological pressure. The Ecological Footprint indicator is used as resource accounting tool that measures how much bioproductive land and water is available. To characterise the sustainable development of a protected landscape area the Ecological Footprint indicator has been used to inform many different audiences and it is portrayed as a powerful educational and prognostic tool; it can facilitate the visualisation and comprehension of human demand on biological resources by expressing the dominant component impact as a proportion of CO2 emissions/ absorption. The ecological Footprint was initially developed and described in the 1990s by Rees and Wackernagel9, 10, 11. It is an estimate of the proportion of the planetary biological productivity and assimilative capacity effectively appropriated by the consumption of the given population or activity over a specified time period - usually a year. In the present paper energy, arable crops, grasslands, forests, water and other resources as evaluative categories have been classified as direct material production and consumption12. Basal area increment modelling is used to predict growth from a potential biological growth function multiplied by a modifier function 13. The Basal Area (BA) term is used as the specific cross-sectional area for each specific above ground biomass-based product. It is a conservative estimate of total product volume/ weight. The factors of biological resources and the respective equivalent factors are used to convert the actual physical area into global hectares or CO 2 equivalents.
a
anthropogenic landscape modification
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3.4. Human development The need for energy efficiency and supply constraints is dominant in the world of today. It has been demonstrated that developed countries with low per capita energy consumption have a high score in the Human Development Index (HDI). The HDI - 0.810 in 2013 ranked Latvia among those countries with a very high human development level14. 3.5. The life cycle benchmark assessment model The environmental simulation model evaluates the environmental impact of activities in a residential environment which can be assessed at different levels which have the following inputs in to consideration: weather external temperature, the sun radiation and precipitation - energy consumed in households - fuel for heating, lighting, business and transport, - energy produced in households by biologically productive land a - such as forest, meadows and alternative energy sources, social such as human, family, city and maintenance and replacement development. 3.5.1. Systems dynamics The systems dynamic simulation model has been created to simplify complex real-world problems for the Environmental Impact and Sustainability Assessment of Residential Development in Protected Areas. A model needs to represent clear understanding of the problems and their interdependence within the real-world system. A system dynamic model is one way to do this, and describes the inter-relation between the subsystems in the system, and also between the components of the subsystems involving feedback interactions. System dynamic models have been recognized as contributing a great deal to the improvement of the decision-making process in strategic planning through policy analysis by allowing the simulation of policy scenarios. Using a system dynamics model, behaviour both in character and in nature of the system can be easily understood, and it is possible to explore all possible development options through the evaluation of the behaviour of the model in given particular scenarios 15. System dynamics is concerned with the significant behavior of a system or a macroscopic view. It helps individuals to understand the aggregate operations of system on a macro-scale. It is very useful for cutting away unnecessary detail and focusing on what is truly important in a model16. The system dynamics method was created by Professor Forrester of Massachusetts Institute of Technology in the mid-1950s17. After decades of development and improvement, the Systemic Dynamics Model has been widely used in the study of the economy, society, ecology and many other complex systems18, 19, 20, 21. The system dynamics approach has provided a theoretical and practical foundation for modelling complex systems in a learning environment so it can be viewed as an effective approach through which different processes can be examined and different scenarios tested from a system perspective. The purpose of the system dynamics modelling approach is to obtain an understanding of, and insights into, system relationships and the search for alternative polices to improve the situation.22 3.5.2. A comparison of some of the existing system dynamics simulation tools The table below presents some general information regarding four system dynamics modeling environments. A brief description of particular simulation software is given in the Table 2:
a
Biologically productive land is land and water (both marine and inland) area that supports significant photosynthetic activity and biomass accumulation that can be used by humans
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Ginta Majore et al. / Procedia Computer Science 43 (2015) 118 – 126 Table 2. Brief comparison of some of the existing system dynamics simulation tools23 Name
Company URL
Demo/run time
Platform
Extendable
Userfriendliness
Learning curve
Comments
STELLA
High Performance
Runtime
Mac/Win
No
5
5
Lots of users. Most used in academic and business
Demo
Win
Somewhat
5
4
Growing fast Weboriented
Demo
Win/Mac
No
5
5
Inexpensive
CS
Runtime
Win
No
5
5
http://www.cherwell.com
Demo
Systems http://www.hps-inc.com POWERSIM
Powersim http://www.powersim.com
VENSIM
Ventana http://www.vensim.com
MODEL MAKER
x POWERSIM is an integrated environment for creating and running simulation models. It uses the block-oriented graphical modelling language taken from the system dynamics method to model a system. The tool uses presentation items such as graphs and tables and has linking capabilities;24, 25, 26 x VENSIM - originally - was developed as a tool for running STELLA models more effectively. It is now a modelling package in its own right, causal loop and system dynamics elements;27 x MODELMAKER is a visual simulation modelling package designed for scientists and engineers28. Models can be structured hierarchically with the definition of sub-models. The package offers – compared with the other systems listed in this section – a wide functionality concerning the analysis of the defined model. Monte Carlo analysis, sensitivity analysis as well as parameter estimation and model optimisation can be calculated using a generic algorithm approach or by a grid search strategy27. 3.5.3. STELLA description STELLA is a modelling tool for building a dynamic simulation models by creating a pictorial diagram of a system and then assigning the appropriate values and mathematical functions to the system. The key objects of STELLA consist of the following four tools: Stocks, Flows, Converters and Connectors. STELLA offers a practical way to dynamically visualize and communicate how complex systems and ideas work in reality. 29 STELLA has been widely used in biological, ecological and environmental sciences 22. It was specifically designed to facilitate the modelling of non-linear, dynamic systems to enhance the learning through scenario testing and analysis. 3.5.4. Model structure in STELLA The purpose of the model in this study was to simulate environmental impact and sustainability assessment of residential development in protected areas. Sustainable assessment requires a long-term perspective, and the level of uncertainty in long-term prediction is high. The time horizon of the model is 50 years. This time horizon is long enough to mean that the future of the system is relatively independent of its initial conditions. The time frame of the calculations is one month. The model consists of eight main components including forest, meadow, swamp, arable land, dwelling house, commercial activities, barn and transport. In Figure 2 it is possible to view the commercial activities sub-model. The amount of CO2 in the commercial activities sub-model is simulated calculating annual CO2 amount for heating the building, usage of electricity and water. The type of heating as well as the percentage increment in commercial activity is taken into account in the calculation of CO2 emissions. The difference in temperature between the temperature in the house and outside is simulated in a separate model and is taken into account in the calculation of the CO2 heating emissions.
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Fig. 2. A commercial activity sub-model.
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4. Discussion 4.1. Landscape metrics The paper has focused on the application of a residential sustainability assessment platform to calculate balance between scenic landscape, above-ground production and human development values which provides information not only regarding the intensity of land, energy and human resource use at this moment by using mobile web based mapping service application. The protected landscape areas consist of land remarkable for original and diverse landscapes and outstanding beauty created to ensure the preservation of the environment appropriate to recreational activities for citizens and tourists. Our model region is situated in the middle of Latvia and is called the Vestiena area where seven European Union protected forest habitats based on the habitat directive 92/43/EEC and one marsh habitat within the protected landscape area of Vestiena. There are in 32 lakes, artificial water constructions - 4 in the protected landscape area Vestiena in the Madona district of Latvia. Many are completely different in their characteristics such as area – from 1 to 407 hectares and in depth – from 1 to 35 meters water composition and their nature and development potential. The significant scenic lakes can be easily reached with a forest mosaic and farmland locations along their banks. Landscape metrics-based analysis was applied on the Vestiena area specific spatial database, which was available on Google Earth through the mobile application. Through the use of relatively simple landscape metrics including areas, relative shares, numbers, coast lengths etc., the transparency and comprehensibility of the assessment and modelling can be reached. A set of landscape metrics indicators was calculated to analyse land use patterns in the selected the ecosystem areas. The naturalness criteria Shape Index (SHAPE) ranged from <1.29 to >1.46. The structural diversity description of scenic beauty according to Shannon’s Diversity Index (SHDI) ranged from <1.06 to >1.73) and patch density (PD) index ranges from <0.29 to >0.67 per km 2. The selected landscape metrics-based indexes express more effectively the relative benefit of differences in spatial boundaries of mosaic land cover. As a result of the fact that, historically, lake estates were built at least 100 m from the lake or in - off lake area, the lakes pollution load was insignificant, and supported the creation of the landscaping. New buildings on the banks of the lakes have a negative effect, both as a source of human waste, as well as the visual changes in the naturally surrounded landscape. Constructions as close as possible to the lake have contaminated the lake landscape not only because of pollution from human economic activity, but also because of the deprivation of the surrounding views created by slopes and floodplains and the countryside in general which has reduced the potential for public use. 4.2. The ecological footprint The major focus of the approach is on the assessment of productivity within biologically productive landscape areas and the impact of human activity on existing and/or proposed new residential development in or adjacent to a protected landscape. The approach adopted in this project can educate users such as: environmentalist, regional developers and planners to enhance their decision making processes and the quality of their decisions regarding sustainable environmental development. The life cycle analysis of the ecological footprint, including above-ground biomass-based product and energy production, the consumption of fossil energy resources, and human development values provide information not only regarding the intensity of the use of land, energy and human resource today, but also future benchmarking values of sustainable protected landscape areas development. 5. Conclusions The scale testing of Goggle Earth based landscape and estate metering and bio-economy life cycle assessment tools could feature the holistic benchmarking of the production of renewable biological resources and their conversion into food, biological products and bioenergy and even waste, e.g. the development of bio-economy to assist society, the economy and nature. Biomass-based product, alternative and fossil energy throughput, as well as ecological footprint related CO2 emissions have good correlation with each other among farmsteads in the protected landscape area, while the human development indicators are site specific. The increased interest in the bio-economy in the protected landscape areas needs to become more specific and measurable. Consequently in order to continue
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to growth sustainable development the major players need to take increased responsibility for their actions. These players include local residents, tourists, real-estate owners, recreation managers, farmers, industrialists and policy makers. A significant challenge for the future is related to the development of GIS joined with simulation task and based on a spatial database for a scenic landscape during its entire life cycle and the spatial action project. References 1. Innovating for Sustainable Growth: A Bioeconomy for Europe. Communication from the Commission to the European Parliament, the Council, the European Economic And Social Committee and the Committee of the Regions. European Commission. Retrieved: 10.10.2014, URL: http://ec.europa.eu/research/bioeconomy/pdf/official-strategy_en.pdf 2. Ode, Å., Fry, G. Tveit, M.S., Messager, P., Miller, D., 2009. Indicators of perceived naturalness as drivers of landscape preference. J. Environ. Plann. Manage. 90, 375–383. 3. De Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272. 4. Herbst, H., Förster, M., Kleinschmit, B., 2009. Contribution of landscape metrics to the assessment of scenic quality – the example of the landscape structure plan Havelland/Gemany. Landscape 10, 1–17 5. Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz Makeschin Assessment of landscape aesthetics—Validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecological Indicators, Volume 32, September 2013, Pages 222-231 6. Blume, H.P., Sukopp, H., 1976. Ökologische Bedeutung anthropogener Bodenveränderungen. Schr. Reihe Vegetationskunde 10, 74–89. 7. Koschke, L., Fürst, C., Frank, S., Makeschin, F., 2012. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning. Ecol. Indicat. 21, 54–66. 8. Europe 2007: Gross Domestic Product & Ecological Footprint 9. Rees, W.E., 1992. Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environment and Urbanization 4, 121–130. 10. Rees, W.E., Wackernagel, M., 1994. Ecological footprints and appropriated carrying capacity: measuring the natural capital requirements of the human economy. In: Jansson, A.M., Hammer, M., Folke, C., Costanza, R. (Eds.), Investing in Natural Capital: the Ecological Economics Approach to Sustainability. Island Press, Washington DC, pp. 362–390. 11. Wackernagel, M., Rees, W., 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers, Gabriola Island, BC. 12. R. Bleischwitz International economics of resource productivity–relevance, measurement, empirical trends, innovation, resource policies, Int. Econ. Policy, 7 (2010), pp. 227–244 13. Hasenauer H., Methodische aspekte bei der Evaluierung von Baummodellen Deutscher Verband forstlicher Versuchsanstalten Ertragenkunde. Beitrag zur Jahres tagung in Volpriehhausen, 19-21 May 1999, pp.45-53. 1999 14. Human Development Report 2014, http://hdr.undp.org/en/2014-report/ 15. Wiranatha A. and Smith P. 2000 A Conceptual Framework for a Dynamic Model for Regional Planning: Towards Sustainable Development for Bali, Indonesia. 1st International Conference on Systems Thinking in Management. 649-654 16. Insight Maker, 2014. Types of modeling. Available from: http://insightmaker.com/modeling 17. Forrester, J.W., 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review 36 (4), 37–66. 18. Chang, Y., Hong, F., Lee, M., 2008. A system dynamic based DSS for sustainable coral reef management in Kenting coastal zone, Taiwan. Ecological Modelling 211(1–2), 153–168. 19. Wang, Y., Zhang, X., 2001. A dynamic modeling approach to simulating socioeconomic effects on landscape changes. Ecological Modelling 140 (1–2), 141–162. 20. Lektauers, A., Trušiņš, J., Trušiņa, I. A Conceptual Framework for Dynamic Modeling of Sustainable Development for Local Government in Latvia. No: Proceedings of the 28th International Conference of the System Dynamics Society, Korejas republika, Seoul, 25.-29. jūlijs, 2010. Seoul: System Dynamics Society, 2010, 1.-14.lpp. ISBN 9781935056065. 21. Tao, Z., 2010. Scenarios of China’s oil consumption per capita (OCPC) using a hybrid Factor Decomposition-System Dynamics (SD) simulation. Energy 35 (1), 168–180. 22. T. Shi, R. Gill. Developing effective policies for the sustainable development of ecological agriculture in China: the case study of Jinshan County with a systems dynamics model. Ecological Economics 53 (2005) 223–246 23. Voinov, A., 1999. Simulation Modeling, Online Course. http://iee.umces.edu/AV/Simmod.html 24. Edelfeldt S. and Fritzson P. 2007 Evaluation and comparison of models and modelling tools simulating nitrogen processes in treatment wetlands. Simulation Modelling Practice and Theory, Elsevier, Volume 16, Issue 1, p.26-49 25. Imagine That. 2013. Overview of ExtendSim. http://www.extendsim.com/prods_overview.html 26. The MathWorks. 2014. Simulink. Simulation and Model-Based Design. http://se.mathworks.com/products/simulink/ 27. Seppelt R. Computer-Based Environmental Management. John Wiley & Sons, 2007. ISBN 3527609210 28. Walker, A. 1997. ModelMaker. Cherwell Scientific, Oxford, U.K 29. Isee System, 2006. Technical document for the iThink and STELLA software. http://www.iseesystems.com 30. Costanza et al., 2002, R. Costanza, A. Voinov, R. Boumans, T. Maxwell, F. Villa, H. Voinov, L. Wainger. Integrated ecological economic modeling of the Patuxent river watershed, Maryland. Ecological Monographs, 72 (2002), pp. 203–231