Migrants are moving in great numbers towards urban areas as a result of urbanization and drivers such as political-, economical- and climate crises. The influx of new people brings new challenges for cities and municipalities to provide for a suitable environment for new and existing citizens whilst preventing adverse effects to occur. Current literature describes the dynamics of the city using effects such as segregation, gentrification and urban decay. However, the changing character of urban areas is not linked with the decision-making behavior of citizens in current literature. Furthermore, the dynamics of urban areas are rarely observed from a meso-scale perspective. This research aims to describe the relation between migration patterns and changes to the fabric of the city. A conceptual model on the dynamics involved in observing decision-making of citizens and observing changes to the city fabric is made. Using real-world data of the city of The Hague in The Netherlands, a Data-Driven Agent-Based model is made in Netlogo. Data analysis on the outcomes of the model using Python provide a better understanding of the impact of migration on the changes to the city fabric. The model output describes the impact on the city caused by the influx of migrants. More migrants entering the city can drastically increase homelessness or the "pushing out" of citizen groups because of housing shortages. This is most prevalent for the poorest citizens, since social rent housing is first to become scarce when more people enter the city. An increase in income inequality or a decrease in average migrant income results in even more pronounced housing shortages. The model simulates 6 policy interventions for the municipality of The Hague. The model shows that all policies that increase housing options significantly increases well-being of citizens. However, the policies that try to improve safety and health do not show an increase in well-being of citizens. By means of historical validation the model is tested for validity. The model is able to describe much of the decision-making behavior of migrants and citizens. However, for certain ethnicities, the model is currently unable to describe the decision-making process in such a way that it can represent the observed real-world moving patterns. A framework has been presented that can be used to better explain the impact of migration on cities. Furthermore, the conceptual model can be applied to different aspects of city dynamics, for instance the transport or energy sector. The ABM shows that current housing options and expected influx of migrants become problematic within the coming 10 years. An indication of the usefulness of proposed policy interventions is presented. More research and surveys are needed to better understand these mechanics and their relation to changes to the fabric of the city. Additional research into the changes to the neighborhood amenities and businesses as a result of changes in citizen composition can aid policy making and understanding the developments of the ever-changing city landscape.

As Needham (2015) concluded based on researching 700 storm surge events: “tropical cyclone induced floods are among the world’s deadliest and destructive natural hazards”. Due to this growing threat, there is a growing need for evacuation policies in case of an impending cyclone, in order to reduce the negative effects of a cyclone. The literature review in this research points out that there are many pre-disaster evacuation models, which focus mostly on either small scale evacuation of a city, or large scale evacuation with the use of motorized vehicles. However, in stretched out rural and developing areas, where access to motorized vehicles is limited or non-existent, residents need ample time to leave the area that is in danger of cyclone induced floods. This means that there should be enough time between the issue of the evacuation order and the moment the cyclone is forecasted to make landfall. However, the sooner the evacuation order is issued, the more uncertainty there is regarding the area that may be subject to the devastating consequences of the cyclone. That means that many residents would be unnecessarily evacuated. This is why the moment of the evacuation order should be at a point where the timeliness of the evacuation order and the uncertainty of the cyclone are optimally balanced. Currently, there does not exist a pre-disaster evacuation model for cyclone-induced floods that accounts for this trade-off. This research uses concepts of existing evacuation models and extends them with the trade-off between timeliness and uncertainty reduction in order to research the added value and applicability of this trade-off. This leads to the following research question: How to balance the trade-off between timeliness and uncertainty reduction in making shelter location decisions in rural and developing areas, under impending cyclone induced floods, while accounting for behavioral aspects of the vulnerable residents? The computer model that is developed to answer the research question consists out of three parts, which are called the building blocks of the model. The first building block translates discrete forecast reports about a cyclone into an area that is vulnerable and should be evacuated, because it will possibly be affected by the cyclone. These forecast reports form the decision points for an evacuation moment. Based on this vulnerable area, possible shelter locations are found in the surroundings of (horizontal evacuation), or on higher grounds within (vertical evacuation), the vulnerable area using a shelter searching algorithm. This algorithm uses a certain safety margin, which is the distance between the possible shelter locations and the vulnerable area to look for possible locations that can shelter the evacuees. The second building block is the optimization part. This sub model optimizes over the complete set of possible shelter locations and selects a given number of shelters that minimizes the weighted distance between the evacuees and the shelters. This weighted distance minimization is also known as the Minisum optimization model (Boonmee, 2017). The third and final building block simulates an evacuation using the previous generated data and captures the results in key performance indicators, so that the effect of the different policy levers can be compared and the balance between timeliness and uncertainty reduction can be found. The models are connected to each other using a Python interface. The model is applied to two different case studies, on which the results and conclusions are based. Three levers that define the shelter location decisions are found relevant in balancing the trade-off between timeliness and uncertainty reduction. The first is the evacuation moment. It shows that a later evacuation moment reduces the number of total evacuees, but it also reduces the evacuees that are saved from the impact of the cyclone. More precisely, the model shows a clear break point, which means that there is a point in time after which it is no longer possible to evacuate all evacuees in danger. The two case studies have shown that this break point is around two days in advance of landfall. This means that the balance in this trade-off lies before this break point. Too early evacuations however, result in a high number of total evacuees, which is not desired as well. This reduction in evacuees over time is not always linear and depends on how the cyclone is forecasted and the characteristics of the geographical area that is under threat. Therefore, it can be concluded that evacuation should happen before the break point, but the exact moment also depends on the forecast reports and the geographical terrain, and is also dependent on the other two levers. However, it is shown that vertical shelter locations significantly reduce the evacuation time and enable later evacuations or evacuations with less shelters. The second lever is the safety margin. This research concludes that a relatively high safety margin is advised in early evacuation moments, but in later evacuation moments it is advised to make use of vertical shelter locations, which means that a low safety margin should be used. The low safety margin is the only way, in later evacuation moments, to save as many evacuees as possible, but it also reduces the accessibility and security of the shelter locations. The third and final lever is the number of shelters. In early evacuation moments, there are many evacuees, which increases the need for sufficient shelters. Therefore, in early evacuations, it is shown that additional shelters have a relatively high reduction in travel distance and high increase of rescued evacuees when compared to later evacuation moments. However, the marginal benefit of an extra shelter is reduced with each additional shelter, which means that the cost of each additional shelter should be balanced against the reduction in travel time and the increase in safely evacuated evacuees. Furthermore, when a distance minimization model is used, the largest shelters will be located closest to the areas with the highest population density. Regarding the sizes of the shelters, later evacuation decisions often means there is need for more shelters. This means that those shelters tend to be smaller, but there will always be larger shelters because of the larger cities. In summary, three policy levers have been identified that define the shelter location decision and that have an impact on the balance between timeliness and uncertainty reduction. None of these levers can single-handedly define how the right balance, and they should therefore be used all-together to define the best balance the trade-off. However, it has also been found that in both case studies the cyclone evolved differently and the geographical area is far from identical as well, which also influences the right balance. This means that every answer about how to balance the trade-off, will also be different in every case. Additionally, it is concluded that the trade-off between timeliness and uncertainty reduction is especially relevant for evacuees who are evacuating by foot. When their travel speed increases, the relevance of the trade-off decreases. This confirms the hypothesis that most evacuation models with motorized vehicles do not account for this trade-off because evacuees have a higher travel speed. Furthermore, this research concludes that when a cyclone is advancing and there is no time to deploy an evacuation model, a heatmap of the population density, together with an elevation map, can give rough estimates of where the largest shelters should be located. The elevation map gives insights into the possible shelter spots because it will point out the elevated locations, either within or outside of the estimated vulnerable area. Those spots that are located closest to the most dense populated areas will probably prove to be suitable shelter locations. Furthermore, in both case studies it is shown that the latest evacuation moment is around two days in advance of landfall of the cyclone and that after that moment it is highly advised to make use of vertical shelter locations. To conclude, this research recommends that the evaluation of the model results will be changed from retrospective to prospective, which means that the model can be deployed in real-time disaster management. Only then, the real value of the model can be shown.

Infectious diseases are responsible for about one quarter of all deaths worldwide and it is likely that major epidemics will emerge in the near future. Through the airline system, diseases can spread around the world rapidly and are more and more likely to do so. At the same time, air connectivity is essential for countries with inadequate land-based infrastructure, in particular during an epidemic when incoming humanitarian assistance is essential. This illustrates the trade-off of passenger airline operations during an epidemic: the likelihood of further spreading of the disease versus the role in combatting that same disease. Policies that prevent infectious diseases from spreading are in place, but knowledge of their effectiveness and impact on the passenger logistics is limited.

The frequency and severity of natural disasters is increasing worldwide, leading to a growing number of people struggling to survive. While climate related natural disasters affect large portions of the world, communities who are already struggling to survive due to conflict, insecurity or poverty are hit the most. In fragile states, slowly unfolding natural disasters are getting more and more intertwined with conflict. In these areas, humanitarian and peacekeeping organizations have increasingly overlapping goals and scarce resources. Sharing information between humanitarian and peacekeeping organizations can improve the effectiveness and efficiency of both humanitarian response operations and peacekeeping missions, which may result in not only saving time and money but most importantly saving lives and reducing human suffering. Nevertheless, the process of information sharing between humanitarian and peacekeeping organizations is not common practice. This is a comprehensive study on the complexities of information sharing between humanitarian and peacekeeping organizations in fragile areas. It includes desk research, interviewing, modeling approaches and a qualitative case study on Mopti, Mali where the Red Cross Movement is actively fighting food insecurity and Dutch peacekeepers are contributing to the UN peacekeeping mission called MINUSMA.

In an emergency, humanitarian organisations share information to prevent redundant data collection and avoid gaps and overlap in the relief activities that they undertake. An analysis of hygiene kit distribution in the Bangladesh-Myanmar displacement crisis and consultation of both literature and humanitarian professionals led to the construction of a model on information diffusion in complex emergencies. This model proved to be able to evaluate strategies that have a level of complexity that could not be apprehended by existing models. Experimentation with this model leads to the conclusion that a locally sourced team, with an outward focused organisation that produces near real-time information products, is most effective in diffusing information.

The frequency of occurrence and the size of natural disaster is increasing, with more people to beaffected in the coming years. The impact is huge, with major infrastructures being damaged andaccess to basic needs cut off for the disaster struck regions. The affected regions and communitiesare dependent on the delivery of disaster relief aid, by governments and non-governmental orga-nizations. The delivery can be difficult, while airports, harbours and other infrastructure is oftendamaged from the disaster.The timely delivery of relief aid to the affected communities is a major challenge of today. Theairports used for the distribution of relief items are often unable to handle the shear size of theinflux of these items. The resources are damaged and personnel might not be able to show up forwork. In order to reduce the overexertion of the local airport, an airbridge can be made with aregional airport. The regional airport can then be used as a buffer, for bundling and storing reliefaid, until it can be delivered to the local airport. The system in which the regional airport receivesthe international contribution of relief items and sends them in a moderated flow to the local airportis called the airport relief chain.The relief chain is subject to several bottlenecks, but due to scarcity in research, the influenceon airport relief chains had not been identified previously. Reducing these bottlenecks and im-proving the relief chain can be achieved by improving the processes on individual airports, as wellas by altering small features in the structure of the relief chain itself. The research question isconsequently:How can the impact of bottlenecks on an airport relief chain system be decreased,considering the airport operations in a fixed network design?To be able to confirm the bottlenecks and investigate possible solutions, a simulation model wasbuilt for the airport relief chain system. To obtain relevant outcomes, data of a real world casestudy was applied to the simulation model. The case study of choice is the earthquake followed bya tsunami in September 2018, that struck the island of Palu, Indonesia. In this disaster, an airportrelief chain system was created, in which the delivery to Mutiara on Palu, serving as the local air-port, was provided through Sepinggan airport on Kalimantan, serving as the regional buffer airport.To this end three bottlenecks were identified for the airport relief chain. namely: (1) The air-craft arrival process at the regional airport, (2) The cargo handling after unloading of the aircraftat the regional airport, and (3) the airbridge connection between the local and regional airport. Thebottleneck at the airbridge is the most stressing, absorbing most of the cargo. Even when the otherbottlenecks are resolved, without improvement in the airbridge the performance will eventually belimited.With the airbridge as a major constraint in the airport relief chain system, the delivery of re-lief items can not be larger than the throughput of the airbridge. The bottlenecks identified at theaircraft arrival and the regional airport processes reduce the flow of relief items even further. Thepolicies investigated are meant to maximize the performance up to the airbridge, creating maximalperformance within the constraints of the system.The performance was measured through the performance indicator, namely the timely delivery.ii
The timely delivery consists oftotal deliveyand theshare of priority demand delivered. The perfor-mance of the system as a whole can as such be evaluated, yet it lacks detail for the implementation ofpolicies. In order to improve the system, the bottlenecks had to be identified, for which the bottle-neck identification measures were introduced. These consist ofidle cargoandthroughput time. Theidle cargo is generally a notification for the occurrence of a bottleneck, with high average idle cargomeaning a bottleneck occurs. The throughput time here indicates whether it was very impacting,as high numbers for throughput time generally mean less impact on the overall component over time.The individual policies were ineffective to truly improve the airport relief chain as a whole. Severalpolicies proved to be quite effective at reducing bottlenecks, thus improving the timely delivery.However, only small improvements were seen in the total delivery, due to the downstream occur-rence of other bottlenecks. Theshare of priority demandalone could be raised effectively throughprioritization policy. Those policies would not provide further system improvements, making theiruse even more case specific.In order to improve on the timely delivery indicators, which includes the total delivery, combina-tions of inventory management policies and airport policies were examined. Based on the individualperformancesdestination prioritizationthrough use of off-airport storage anditem prioritizationon the most stressing relief type were chosen as the inventory management policies. These, if pairedwithadditional workerspolicies, were able to absolve the bottlenecks in the system up to the air-bridge. The combinations as such performed at the optimal range, considering the final constrainton the system and are preferable for implementation.The prioritization policies were the most useful, but require staff and their equipment to fullyperform. As such the processes in an airbridge system for the provision of relief items should con-sider the capacity at the regional airport and optimize the processes accordingly up to the airbridgeitself.

Complex and uncertain societal problems cannot be addressed by technical solutions that rely solely on predictions. Institutions that entirely rely on predictions, repeat the same actions (routine), with little reflection on the impact of these technological solutions upon the socio-technical system. Though routine is beneficial for stability and continuity of any institution, it may stifle reflection and make it harder for change. When an institution does not change, it cannot innovate nor adapt to changing circumstances.

Social learning (SL) has been proposed to facilitate institutional change. SL is a change in societal understanding, achieved through social interactions, which eventually gets situated within broader social networks. In principle, SL holds a promise in addressing the problem of routinised, non-adaptive institutions. Nevertheless, there is limited evidence on whether SL does indeed lead to institutional change.

This PhD research uses policy gaming to assess whether SL can lead to institutional change in the Nzoia River Basin. The results indicate that SL has the potential to change routine-based institutions and generate adaptive capacity. The outcomes also indicate the need for the following profound institutional changes in Nzoia River Basin:

Artefacts: Replace current WRM structures with configurations that respect the river, and support the sustainable management of the drainage basin, as a whole.

Values: Value water more than spatial, agricultural and energy-production plans and make water the structuring element within the Nzoia River Basin. This means that any proposed laws, regulations, practices and norms that intend to utilise the scarce water resources unsustainably should not be supported.

Underlying Assumptions: Question underlying assumptions, and make transformations to existing laws, regulations, values, norms and actor-networks to build adaptive capacity.@en

Weather forecast agencies periodically provide information on potential typhoon behavior from its formation to dissipation. This information can be used to pre-position relief goods in areas that are potentially affected by a strong incoming typhoon. The uncertainty of the typhoon behavior with respect to a point location decreases over time, thus showing the trade-off between forecast accuracy and lead time available for pre-positioning. This thesis paper presents a iterative methodology that uses a forecast-driven facility location model for pre-positioning over large networks where lead time available for pre-positioning at each location can differ. By periodically generating damage scenarios based on new forecast data and eliciting the strategic preference of the decision maker, the model determines where, when, and how much relief goods to pre-position. The model is implemented in a case study to design the pre-positioning actions in anticipation of typhoon Haiyan in the Philippines.

Airports are a special critical infrastructure in a disaster response from a resilience point of view. They must overcome two types of shocks when they are affected by a disaster. They are not only hit by a disaster, which decreases their performances. Airports also need to act as a humanitarian and logistic hub for the disaster response which leads to an influx of aircraft. This influx requires the airport to increase their normal performance. In this research a mesoscopic model is built to compare different policies that can improve the resilience of the operations of airports during the immediate post disaster response.

To compare the effect of the policies on the level of resilience of the system, this study proposes a new resilient measurement approach that incorporates the bounce back and bounce up capacity of a system. This approach divides resilience into three aspects (1) absorption capacity, (2) adaptive capacity and (3) recovery time. This resilience measurement approach can be used when a system has the following two characteristics: (1) dynamic required service levels over time and (2) internal system changes.

The natural river deltas around the world are expeeriencing changes due to the relative sea level rise and the large number of people settled around these fertile areas are experiencing threat to the livelihoods. Various research intiatives stem from the warning signals in these regions and governments attempt to re-align their strategic vision to support the long-term development of these regions. However, once the strategic plans are formalized, the implmentation does not always go as intended, due to the lack of local context, institutional arrangements, and availability of resources.
In this research, an attempt is made to structure the process of implementation by proposing a frmaework to keep track of the implementation process, as the project moves forward. The framework is then operationalized for a province in the Mekong Delta of Vietnam, where the developments are studied and compared with the strategic objectives of the region.

In the response phase of sudden-onset natural disasters, information is of crucial importance for relief organisations and their aid workers. Information is necessary for gaining situational awareness in a disaster for effective decision-making in response operations. In my research, an information system is designed that maps the reachability of affected communities in a disaster-struck area, in terms of their connectedness to relief efforts by aid workers. This reachability model can be deployed rapidly and offers aid workers a situational overview in the first moments of the response, giving them relevant information to base their day-to-day decision on. The reachability model is built in Python, using graph theory and OpenStreetMap. The Papua New Guinea earthquake of February 2018 and hurricane Irma on St. Maarten are used as case-studies to evaluate the feasibility of the reachability model. The conclusion of the research is that the reachability model is both technically and practically feasible and that it improves situational awareness for aid workers and therewith improve the effectiveness of relief operations during the response phase of sudden-onset natural disasters.

Situation: Information management enables humanitarian organizations to make adequate interventions based on timely, appropriate and trustworthy information. A crucial type of information are identities, because they can be used to assess vulnerability and efficiently manage aid distribution. Vulnerability determines who receives aid first because resources are always limited. This information is increasingly being stored and processed in identity systems.
Complication: Most identity systems are centralized and produce analogue proofs of identity such as passports or ID cards. These systems are susceptible to privacy and data breaches. Centralization leads to single-points-of-failure and could lead to fraudulent behavior resulting in people lacking formal proofs of identity. In general there is limited interoperability between identity systems and limited collaboration between the owners of these systems.
Approach: To create an interoperable and shared digital identity system using a Design Science Research strategy and systems engineering approach. This system must be distributed, protect privacy and put the identity owner in control of his or her data. The foundation of the system consists of Humanitarian Information Management principles, Privacy-by-Design principles and Self-Sovereign Identity principles. This research creates a functional blockchain based system, that enables identities for the use-case of Cash Transfer Programs.
Results: We present a validated set of ten design decisions that represent the trade-offs that have been made and prescribe a blueprint for a technical design.
Next steps:Future research should be done on how such a system could be implemented and used. This would require a process design approach that has to be developed, Also, elaborate research into user experience and user interfaces should be conducted.

Humanitarian assistance is driven by data and information. Through the whole
chain of actions – from early warning systems to evaluation – information
determines priorities, resource allocation, and donors’ willingness to donate.
However, the potential harm that comes with data is often overlooked. Inadequate
data management increases the potential of data to harm the same people
humanitarians are trying to help. The goal of the presented research is to identify
the characteristics and mechanisms that influence humanitarian interaction,
with the aim to find those mechanisms that can influence humanitarians towards
better information security. The most influential characteristics are analysed in
order to determine what is likely to increase information security sector-wide.

An agent-based modelling study, which shows the impact of predefined policies on a stylized case study of Syria.

On a daily basis we (in) directly make use of functions of various infrastructures: we drive on roads and bridges or we live without worries behind the safety of a dike. Only when there are cracks in the dike or a particular bridge is not accessible due to for example a broken pillar, we realize how critical these infrastructures are for our society and economy. These infrastructures are not build to last forever and they are susceptible to breakdowns, especially as some are reaching the end of their technical lifespan. Building new infrastructures is simply not an option, as this is often financially and logistically not feasible. Getting as much as possible out of the remaining technical lifespan of such critical infrastructures is then down to employing innovative maintenance strategies, such as risk and opportunity based asset management, as is being done in the ROBAMCI project, initiated by Deltares. Management and maintenance of infrastructures already takes place in a multi-actor environment. Adding systematic approaches on top of this multi-actor environment requires maintenance managers to further integrate their decision making processes. However, analysis have shown that there is a clear lack of this integrated decision making. Literature has further shown that this could be as a result of a lack of information sharing between organizations. Improving inter-organizational information sharing, however first requires further understandings. This research project is done with the goal to achieve that understanding. A case-study in the context of ROBAMCI was used to study this concpt in a practical environment. The asset in this case was a sea-lock complex located in Delfzijl, which not only performs functions for shipping, but also has various other direct and indirect functions, influencing for example local nature, economy and regional water management. Theoretical knowledge, gained from literature studies, was then used to interpret the data from in-depth interviews with employees of identified organizations. The empirical findings, interpreted through various sources of literature showed that there is a wide array of factors influencing inter-organizational information sharing in the case-study. Among the most mentioned and expected were trust, competing interests/self-interests and legislations & policies. Organizations also add value to the importance of personal physical interactions and even though the information sharing takes place between actors of one national government, concern for information misuse is also seen as a potential barrier for information sharing. The data has also led to the formation of some factor-relations, we see e.g. that trust forms a positive causal relation with not only information sharing itself, but also has a negative causal relation with concerns of the quality of the information. Data also makes it clear that there is an interrelation between the 3 levels of information sharing. These findings, backed by theory have led to the identification of 2 general fundamentals that can be taken into account when trying to further understand information sharing. The first one focuses on trust and legislations, whereas the second one addresses on the embeddedness of the information sharing levels. Conclusively it can be said that inter-organizational information sharing helps to make more efficient and effective decisions for the management and maintenance of assets. There are many factors that can either positively or negatively influence information sharing. It is recommended that through further studying factor relations their importance can be determined. This can also help to reduce the high number of factors and frameworks. For ROBAMCI it is recommended to use the results of this research as input for a maturity analysis focusing on the inter-organizational information management of organizations in the sector.

The incidence of natural disasters worldwide is increasing. As a result, there is a growing number of people in need, whereas limited resources to help these people are available. It is therefore important to effectively and efficiently prioritize the most vulnerable people in the preparedness phase, and the most affected people in the response phase of humanitarian action. Data-driven models have the potential to help in doing so. However, to be able to apply these models in a country, a certain level of data preparedness is required. Therefore, there is a need to understand how to facilitate, stimulate and coordinate data-sharing between humanitarian actors. To realize this, a generic governance structure is needed that can facilitate the achievement of this level of data preparedness on a large scale. This thesis aims to develop this generic governance structure using a data ecosystem perspective, by providing insight in the success criteria for establishing a ‘humanitarian data ecosystem’. As no scientific literature exists on this topic, as a first step, a general framework with data ecosystem governance success criteria is developed by means of a systematic literature review. Subsequently, the applicability of this framework in the humanitarian sector is assessed through a case study on the ‘Community Risk Assessment and Prioritization toolbox developed by 510 Global, the data team of the Netherlands Red Cross. It is found that the data ecosystem approach provides a suitable framework for assessing the criteria to be addressed when aiming to establish a successful humanitarian data ecosystem. Moreover, the empirical findings resulting from the case study led to the creation of a generic governance structure to be used when determining on the approach to adopt when rolling out the Community Risk Assessment and Prioritization toolbox in a new country.

India is a country constantly grappling with problems of impeded rural development. Energy, in particular sustainable energy can be seen as key to attain the Sustainable Development Goals set out by the U.N. Integration of rural sectors can be seen as a methodology to broaden the potential achievement of the targetted Sustainable Development Goals. This thesis report aims at providing multi-purpose systems as a result of energy focused integration of sectors for augmenting rural development in India. The main research question of the thesis was ’Having recognized the importance of systems thinking, how can energy focussed Integration of Sectors provide directions to overcome the problem of impeded rural development in India?’. Top-Down knowledge gaps are prevalent in India for such integrative techniques, thus requiring attention to primarily to make integration of sectors a reality in India, thus the thesis has a core around marking out potential barriers which could be faced by such sector integrated techniques to break into the Indian market and policy while discussing the obstacles to implementation of such solutions. Furthermore another important aspect of the thesis lays out some plausible solutions to these barriers and obstacles.